{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Hill-Tononi Neuron and Synapse Models\n",
    "\n",
    "## Hans Ekkehard Plesser, NMBU/FZ Jülich/U Oslo, 2016-12-01, 2021-02-22\n",
    "\n",
    "## Background\n",
    "\n",
    "This notebook describes the neuron and synapse model proposed by Hill and Tononi in *J Neurophysiol* 93:1671-1698, 2005 ([doi:10.1152/jn.00915.2004](http://dx.doi.org/doi:10.1152/jn.00915.2004)) and their implementation in NEST. The notebook also contains some tests.\n",
    "\n",
    "This description is based on the original publication and publications cited therein, an analysis of the source code of the original Synthesis implementation kindly provided by Sean Hill, and plausibility arguments.\n",
    "\n",
    "In what follows, we will refer to the original paper as [HT05].\n",
    "\n",
    "For a NEST implementation of the full network model by Hill and Tononi see the [implementation by Ricardo Muprhy and colleagues](https://github.com/ricardomurphy/Multiarea-Hill-Tononi-thalamocortical-network-model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Neuron Model\n",
    "\n",
    "### Integration \n",
    "\n",
    "The original Synthesis implementation of the model uses Runge-Kutta integration with fixed 0.25 ms step size, and integrates channels dynamics first, followed by integration of membrane potential and threshold.\n",
    "\n",
    "NEST, in contrast, integrates the complete 16-dimensional state using a single adaptive-stepsize Runge-Kutta-Fehlberg-4(5) solver from the GNU Science Library (`gsl_odeiv_step_rkf45`).\n",
    "\n",
    "### Membrane potential\n",
    "\n",
    "Membrane potential evolution is governed by [HT05, p 1677]\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}V}{\\text{d}t} = \\frac{-g_{\\text{NaL}}(V-E_{\\text{Na}})\n",
    "-g_{\\text{KL}}(V-E_{\\text{K}})+I_{\\text{syn}}+I_{\\text{int}}}{\\tau_{\\text{m}}}\n",
    "-\\frac{g_{\\text{spike}}(V-E_{\\text{K}})}{\\tau_{\\text{spike}}}\n",
    "\\end{equation}\n",
    "\n",
    "- The equation does not contain membrane capacitance. As a side-effect, all conductances are dimensionless.\n",
    "- Na and K leak conductances $g_{\\text{NaL}}$ and $g_{\\text{KL}}$ are constant, although $g_{\\text{KL}}$ may be adjusted on slow time scales to mimic neuromodulatory effects.\n",
    "- Reversal potentials $E_{\\text{Na}}$ and $E_{\\text{K}}$ are assumed constant.\n",
    "- Synaptic currents $I_{\\text{syn}}$ and intrinsic currents $I_{\\text{int}}$ are discussed below. In contrast to the paper, they are shown with positive sign here (just change in notation).\n",
    "- The last term is a re-polarizing current only active during the refractory period, see below. Note that it has a different (faster) time constant than the other currents. It might have been more natural to use the same time constant for all currents and instead adjust $g_{\\text{spike}}$. We follow the original approach here."
   ]
  },
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   "metadata": {},
   "source": [
    "### Threshold, Spike generation and refractory effects\n",
    "\n",
    "The threshold evolves according to [HT05, p 1677]\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}\\theta}{\\text{d}t} = -\\frac{\\theta-\\theta_{\\text{eq}}}{\\tau_{\\theta}}\n",
    "\\end{equation}\n",
    "\n",
    "The neuron emits a single spike if \n",
    "\n",
    "- it is not refractory\n",
    "- membrane potential crosses the threshold, $V\\geq\\theta$\n",
    "\n",
    "Upon spike emission,\n",
    "\n",
    "- $V \\leftarrow E_{\\text{Na}}$\n",
    "- $\\theta \\leftarrow E_{\\text{Na}}$\n",
    "- the neuron becomes refractory for time $t_{\\text{spike}}$ (`t_ref` in NEST)\n",
    "\n",
    "The repolarizing current is active during, and only during the refractory period:\n",
    "\\begin{equation}\n",
    "g_{\\text{spike}} = \\begin{cases}  1  & \\text{neuron is refractory}\\\\\n",
    "0 & \\text{else} \\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "During the refractory period, the neuron cannot fire new spikes, but all state variables evolve freely, nothing is clamped. \n",
    "\n",
    "The model of spiking and refractoriness is based on Synthesis model `PulseIntegrateAndFire`."
   ]
  },
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   "metadata": {},
   "source": [
    "### Intrinsic currents\n",
    "\n",
    "Note that not all intrinsic currents are active in all populations of the network model presented in [HT05, p1678f].\n",
    "\n",
    "Intrinsic currents are based on the Hodgkin-Huxley description, i.e.,\n",
    "\n",
    "\\begin{align}\n",
    "I_X &= g_{\\text{peak}, X} m_X(V, t)^N_X h_X(V, t)(V-E_X) \\\\\n",
    "\\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m_X^{\\infty}-m_X}{\\tau_{m,X}(V)}\\\\\n",
    "\\frac{\\text{d}h_X}{\\text{d}t} &= \\frac{h_X^{\\infty}-h_X}{\\tau_{h,X}(V)}\n",
    "\\end{align}\n",
    "\n",
    "where $I_X$  is the current through channel $X$ and $m_X$ and $h_X$ the activation and inactivation  variables for channel $X$.\n",
    "\n",
    "#### Pacemaker current $I_h$\n",
    "\n",
    "Synthesis: `IhChannel`\n",
    "\n",
    "\\begin{align}\n",
    "N_h & = 1 \\\\\n",
    "m_h^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(\\frac{V+75\\text{mV}}{5.5\\text{mV}}\\right)} \\\\\n",
    "\\tau_{m,h}(V) &= \\frac{1}{\\exp(-14.59-0.086V) + \\exp(-1.87  + 0.0701V)} \\\\\n",
    "h_h(V, t) &\\equiv 1 \n",
    "\\end{align}\n",
    "\n",
    "Note that subscript $h$ in some cases above marks the $I_h$ channel."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low-threshold calcium current $I_T$\n",
    "\n",
    "Synthesis: `ItChannel`\n",
    "\n",
    "##### Equations given in paper \n",
    "\n",
    "\\begin{align}\n",
    "N_T & \\quad \\text{not given} \\\\\n",
    "m_T^{\\infty}(V) &= 1/\\{1 +  \\exp[ -(V +  59.0)/6.2]\\} \\\\\n",
    "\\tau_{m,T}(V) &= \\{0.22/\\exp[ -(V  + 132.0)/ 16.7]\\} +  \\exp[(V  + 16.8)/18.2] +  0.13\\\\\n",
    "h_T^{\\infty}(V) &= 1/\\{1 +  \\exp[(V +  83.0)/4.0]\\} \\\\\n",
    "\\tau_{h,T}(V) &= \\langle  8.2 +  \\{56.6 +  0.27 \\exp[(V +  115.2)/5.0]\\}\\rangle / \\{1.0 +  \\exp[(V +  86.0)/3.2]\\}\n",
    "\\end{align}\n",
    "\n",
    "Note the following:\n",
    "- The channel model is based on Destexhe et al, *J Neurophysiol* 76:2049 (1996).\n",
    "- In the equation for $\\tau_{m,T}$, the second exponential term must be added to the first (in the denominator) to make dimensional sense; 0.13 and 0.22 have unit ms.\n",
    "- In the equation for $\\tau_{h,T}$, the $\\langle \\rangle$ brackets should be dropped, so that $8.2$ is not divided by the $1+\\exp$ term. Otherwise, it could have been combined with the $56.6$.\n",
    "- This analysis is confirmed by code analysis and comparison with Destexhe et al, *J Neurophysiol* 76:2049 (1996), Eq 5.\n",
    "- From Destexhe et al we also find $N_T=2$.\n",
    "\n",
    "##### Corrected equations\n",
    "\n",
    "This leads to the following equations, which are implemented in Synthesis and NEST.\n",
    "\n",
    "\\begin{align}\n",
    "N_T &= 2 \\\\\n",
    "m_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(-\\frac{V+59\\text{mV}}{6.2\\text{mV}}\\right)}\\\\\n",
    "\\tau_{m,T}(V) &= 0.13\\text{ms} \n",
    "+ \\frac{0.22\\text{ms}}{\\exp\\left(-\\frac{V  + 132\\text{mV}}{16.7\\text{mV}}\\right) + \\exp\\left(\\frac{V +  16.8\\text{mV}}{18.2\\text{mV}}\\right)} \\\\ \n",
    "h_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(\\frac{V+83\\text{mV}}{4\\text{mV}}\\right)}\\\\\n",
    "\\tau_{h,T}(V) &= 8.2\\text{ms} +  \\frac{56.6\\text{ms} +  0.27\\text{ms} \\exp\\left(\\frac{V   + 115.2\\text{mV}}{5\\text{mV}}\\right)}{1 +   \\exp\\left(\\frac{V  + 86\\text{mV}}{3.2\\text{mV}}\\right)}\n",
    "\\end{align}\n",
    "\n",
    "**Note:** $N_T$ is a settable parameter in NEST."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Persistent Sodium Current $I_{\\text{NaP}}$\n",
    "\n",
    "Synthesis: `INaPChannel`\n",
    "\n",
    "This model has only activation ($m$) and uses the steady-state value, so the only relevant equation is that for $m$. In the paper, it is given as\n",
    "\n",
    "\\begin{equation}\n",
    "m_{\\text{NaP}}^{\\infty}(V) = 1/[1+\\exp(-V+55.7)/7.7]\n",
    "\\end{equation}\n",
    "\n",
    "Dimensional analysis indicates that the division by $7.7$ should be in the argument of the exponential, and the minus sign needs to be moved so that the current activates as the neuron depolarizes leading to the corrected equation\n",
    "\n",
    "\\begin{equation}\n",
    "m_{\\text{NaP}}^{\\infty}(V) = \\frac{1}{1+\\exp\\left(-\\frac{V+55.7\\text{mV}}{7.7\\text{mV}}\\right)}\n",
    "\\end{equation}\n",
    "\n",
    "This equation is implemented in NEST and Synthesis and is the one found in Compte et al (2003), cited by [HT05, p 1679].\n",
    "\n",
    "##### Corrected exponent\n",
    "\n",
    "According to Compte et al (2003), $N_{\\text{NaP}}=3$, i.e.,\n",
    "\\begin{equation}\n",
    "I_{\\text{NaP}} = g_{\\text{peak,NaP}}(m_{\\text{NaP}}^{\\infty}(V))^3(V-E_{\\text{NaP}})\n",
    "\\end{equation}\n",
    "This equation is also given in a comment in Synthesis, but is missing from the implementation.\n",
    "\n",
    "**Note:** NEST implements the equation according to Compte et al (2003) with $N_{\\text{NaP}}=3$ by default, while Synthesis uses $N_{\\text{NaP}}=1$. $N_{\\text{NaP}}$ is a settable parameter in NEST."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Depolarization-activated Potassium Current $I_{DK}$\n",
    "\n",
    "Synthesis: `IKNaChannel`\n",
    "\n",
    "This model also only has a single activation variable $m$, following more complicated dynamics expressed by $D$.\n",
    "\n",
    "##### Equations in paper\n",
    "\n",
    "\\begin{align}\n",
    "dD/dt &= D_{\\text{influx}} - D(1-D_{\\text{eq}})/\\tau_D \\\\\n",
    "D_{\\text{influx}} &= 1/\\{1+ \\exp[-(V-D_{\\theta})/\\sigma_D]\\} \\\\\n",
    "m_{DK}^{\\infty} &= 1/1 + (d_{1/2}D)^{3.5}\n",
    "\\end{align}\n",
    "\n",
    "There are several problems with these equations.\n",
    "\n",
    "In the steady state the first equation becomes\n",
    "\\begin{equation}\n",
    "0 = - D(1-D_{\\text{eq}})/\\tau_D \n",
    "\\end{equation}\n",
    "with solution\n",
    "\\begin{equation}\n",
    "D = 0\n",
    "\\end{equation}\n",
    "This contradicts both the statement [HT05, p. 1679] that $D\\to D_{\\text{eq}}$ in this case, and the requirement that $D>0$ to avoid a singluarity in the equation for $m_{DK}^{\\infty}$. The most plausible correction is\n",
    "\\begin{equation}\n",
    "dD/dt = D_{\\text{influx}} - (D-D_{\\text{eq}})/\\tau_D \n",
    "\\end{equation}\n",
    "\n",
    "The third equation appears incorrect and logic as well as Wang et al, *J Neurophysiol* 89:3279–3293, 2003, Eq 9, cited in [HT05, p 1679], indicate that the correct equation is\n",
    "\n",
    "\\begin{equation}\n",
    "m_{DK}^{\\infty} = 1/(1 + (d_{1/2} / D)^{3.5})\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "\n",
    "##### Corrected equations\n",
    "\n",
    "The equations for this channel implemented in NEST are thus\n",
    "\n",
    "\\begin{align}\n",
    "I_{DK} &= - g_{\\text{peak},DK} m_{DK}(V,t) (V - E_{DK})\\\\\n",
    "m_{DK} &= \\frac{1}{1 + \\left(\\frac{d_{1/2}}{D}\\right)^{3.5}}\\\\\n",
    "\\frac{dD}{dt} &= D_{\\text{influx}}(V) - \\frac{D-D_{\\text{eq}}}{\\tau_D} = \\frac{D_{\\infty}(V)-D}{\\tau_D} \\\\\n",
    "D_{\\infty}(V) &= \\tau_D D_{\\text{influx}}(V) + {D_{\\text{eq}}}\\\\\n",
    "D_{\\text{influx}} &= \\frac{D_{\\text{influx,peak}}}{1+ \\exp\\left(-\\frac{V-D_{\\theta}}{\\sigma_D}\\right)} \n",
    "\\end{align}\n",
    "\n",
    "with \n",
    "\n",
    "|$D_{\\text{influx,peak}}$|$D_{\\text{eq}}$|$\\tau_D$|$D_{\\theta}$|$\\sigma_D$|$d_{1/2}$|\n",
    "| --: | --: | --: | --: | --: | --: |\n",
    "|$0.025\\text{ms}^{-1}$ |$0.001$|$1250\\text{ms}$|$-10\\text{mV}$|$5\\text{mV}$|$0.25$|\n",
    "\n",
    "Note the following:\n",
    "- $D_{eq}$ is the equilibrium value only for $D_{\\text{influx}}(V)=0$, i.e., in the limit $V\\to -\\infty$ and $t\\to\\infty$.\n",
    "- The actual steady-state value is $D_{\\infty}$.\n",
    "- $d_{1/2}$, $D$, $D_{\\infty}$, and $D_{\\text{eq}}$ have identical, but arbitrary units, so we can assume them dimensionless ($D$ is a \"factor\" that in an abstract way represents concentrations).\n",
    "- $D_{\\text{influx}}$ and $D_{\\text{influx,peak}}$ are rates of change of $D_{\\infty}$ and thus have units of inverse time.\n",
    "- $m_{DK}$ is a steep sigmoid which is almost 0 or 1 except for a narrow window around $d_{1/2}$.\n",
    "- To the left of this window, $I_{DK}\\approx 0$.\n",
    "- To the right of this window, $I_{DK}\\sim -(V-E_{DK})$.\n",
    "- $m_{DK}$ is not integrated over time, instead it is an instantaneous transform of $D$, which is integrated over time.\n",
    "\n",
    "**Note:** The differential equation for $dD/dt$ differs from the one implemented in Synthesis."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synaptic channels\n",
    "\n",
    "These are described in [HT05, p 1678]. Synaptic channels are conductance based with double-exponential time course (beta functions) and normalized for peak conductance. NMDA channels are additionally voltage gated, as described below.\n",
    "\n",
    "Let $\\{t_{(j, X)}\\}$ be the set of all spike arrival times, where $X$ indicates the synapse model and $j$ enumerates spikes. Then the total synaptic input is given by\n",
    "\n",
    "\\begin{equation}\n",
    "I_{\\text{syn}}(t) = - \\sum_{\\{t_{(j, X)}\\}} \\bar{g}_X(t-t_{(j, X)}) (V-E_X)\n",
    "\\end{equation}\n",
    "\n",
    "#### Standard Channels\n",
    "\n",
    "Synthesis: `SynChannel`\n",
    "\n",
    "The conductance change due to a single input spike at time $t=0$ through a channel of type $X$ is given by (see below for exceptions)\n",
    "\n",
    "\\begin{align}\n",
    "\\bar{g}_X(t) &= g_X(t)\\\\\n",
    "g_X(t) &= g_{\\text{peak}, X}\\frac{\\exp(-t/\\tau_1) - \\exp(-t/\\tau_2)}{\n",
    "\\exp(-t_{\\text{peak}}/\\tau_1) - \\exp(-t_{\\text{peak}}/\\tau_2)} \\Theta(t)\\\\\n",
    "t_{\\text{peak}} &= \\frac{\\tau_2 \\tau_1}{\\tau_2 - \\tau_1} \\ln\\frac{ \\tau_2}{\\tau_1}\n",
    "\\end{align} \n",
    "\n",
    "where $t_{\\text{peak}}$ is the time of the conductance maximum and $\\tau_1$ and $\\tau_2$ are synaptic rise- and decay-time, respectively; $\\Theta(t)$ is the Heaviside step function. The equation is integrated using exact integration in Synthesis; in NEST, it is included in the ODE-system integrated using the Runge-Kutta-Fehlberg 4(5) solver from GSL.\n",
    "\n",
    "The \"indirection\" from $g$ to $\\bar{g}$ is required for consistent notation for NMDA channels below.\n",
    "\n",
    "These channels are used for AMPA, GABA_A and GABA_B channels.\n",
    "\n",
    "#### NMDA Channels\n",
    "\n",
    "Synthesis: `SynNMDAChannel`\n",
    "\n",
    "For the NMDA channel we have\n",
    "\\begin{equation}\n",
    "\\bar{g}_{\\text{NMDA}}(t) = m(V, t) g_{\\text{NMDA}}(t)\n",
    "\\end{equation}\n",
    "with $g_{\\text{NMDA}}(t)$ from above. \n",
    "\n",
    "The voltage-dependent gating $m(V, t)$ is defined as follows (based on textual description, Vargas-Caballero and Robinson *J Neurophysiol* 89:2778–2783, 2003, [doi:10.1152/jn.01038.2002](http://dx.doi.org/10.1152/jn.01038.2002), and code inspection):\n",
    "\n",
    "\\begin{align}\n",
    "m(V, t) &= a(V) m_{\\text{fast}}^*(V, t) + ( 1 - a(V) ) m_{\\text{slow}}^*(V, t)\\\\\n",
    "a(V)    &= 0.51 - 0.0028 V \\\\\n",
    "m^{\\infty}(V) &= \\frac{1}{ 1 + \\exp\\left( -S_{\\text{act}} ( V - V_{\\text{act}} ) \\right) } \\\\\n",
    "m_X^*(V, t) &= \\min(m^{\\infty}(V), m_X(V, t))\\\\\n",
    "\\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m^{\\infty}(V) - m_X }{ \\tau_{\\text{Mg}, X}}\n",
    "\\end{align} \n",
    "\n",
    "where $X$ is \"slow\" or \"fast\". $a(V)$ expresses voltage-dependent weighting between slow and fast unblocking, $m^{\\infty}(V)$ the steady-state value of the proportion of unblocked NMDA-channels, the minimum condition in $m_X^*(V,t)$ the instantaneous blocking and the differential equation for $m_X(V,t)$ the unblocking dynamics.\n",
    "\n",
    "Synthesis uses tabluated values for $m^{\\infty}$. NEST uses the best fit of $V_{\\text{act}}$ and $S_{\\text{act}}$  to the tabulated data for conductance table `fNMDA`.\n",
    "\n",
    "**Note**: NEST also supports instantaneous NMDA dynamics using a boolean switch. In that case $m(V, t)=m^{\\infty}(V)$. "
   ]
  },
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   "metadata": {},
   "source": [
    "##### Detailed relation between NMDA channel model in NEST and previous work\n",
    "\n",
    "The NMDA channel dynamics are not very clearly described in the original paper by Hill and Tononi. The model implemented in NEST is at this point mostly based on inspecting the code of Sean Hill's Synthesis simulator. \n",
    "\n",
    "The equations for $m(V,t)$ and $a(V)$ above are based on Eq 2 of Vargas-Caballero and Robinson (2003) [VCR in the following] and text below that equation, from which the slow and fast NMDA unblocking time constants are also taken. The exponential time courses in VCR Eq 2 are in NEST modeled by the differential equation above.  \n",
    "\n",
    "Further, the logic is as follows: $m \\to 0$ corresponds to blocking of NMDA channels. This happens for small values of $V$, i.e., $V < V_{\\text{act}}$. Blocking is assumed to be instantaneous, and this is implented by the minimum operation in the equation for $m^*_X(V, t)$ above.  What remains is the equation for $m^{\\infty}(V)$ above, giving the steady-state blocking of the NMDA channel for a given voltage. The equation here is based on VCR Eq 1, with our $V_{\\text{act}}$ corresponding to their $V_{0.5}$ and our $S_{\\text{act}}$ corresponding to their $\\frac{z \\delta F}{RT}$ (I believe some parentheses in VCR Eq 1 are incorrectly placed), see also VCR Fig 1B.  So $V_{\\text{act}}$ is the voltage for which $m^{\\infty}(V) = \\frac{1}{2}$ and $S_{\\text{act}}$ determines the slope of the sigmoidal activation curve, with large $S_{\\text{act}}$ corresponding to a steeper curve.  \n",
    "\n",
    "Synthesis implemented this equation using a look-up table. I obtained the parameter values for $V_{\\text{act}}$ and $S_{\\text{act}}$ in `ht_neuron` by fitting the equation for $m^{\\infty}(V)$ to this lookup table. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No synaptic \"minis\"\n",
    "\n",
    "Synaptic \"minis\" due to spontaneous release of neurotransmitter quanta [HT05, p 1679] are not included in the NEST implementation of the Hill-Tononi model, because the total mini input rate for a cell was just 2 Hz and they cause PSP changes by $0.5 \\pm 0.25$mV only and thus should have minimal effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Synapse Depression Model\n",
    "\n",
    "The synapse depression model is implemented in NEST as `ht_synapse`, in Synthesis in `SynChannel` and `VesiclePool`.\n",
    "\n",
    "$P\\in[0, 1]$ describes the state of the presynaptic vesicle pool. Spikes are transmitted with an effective weight\n",
    "\\begin{equation}\n",
    "w_{\\text{eff}} = P w\n",
    "\\end{equation}\n",
    "where $w$ is the nominal weight of the synapse.\n",
    "\n",
    "### Evolution of $P$ in paper and Synthesis implementation\n",
    "\n",
    "According to [HT05, p 1678], the pool $P$ evolves according to\n",
    "\\begin{equation}\n",
    "\\frac{\\text{d}P}{\\text{d}t} = -\\:\\text{spike}\\:\\delta_P P+\\frac{P_{\\text{peak}}-P}{\\tau_P}\n",
    "\\end{equation}\n",
    "where\n",
    "- $\\text{spike}=1$ while the neuron is in spiking state, 0 otherwise\n",
    "- $P_{\\text{peak}}=1$ \n",
    "- $\\delta_P = 0.5$ by default\n",
    "- $\\tau_P = 500\\text{ms}$ by default\n",
    "Since neurons are in spiking state for one integration time step $\\Delta t$, this suggest that the effect of a spike on the vesicle pool is approximately\n",
    "\\begin{equation}\n",
    "P \\leftarrow ( 1 - \\Delta t \\delta_P ) P\n",
    "\\end{equation}\n",
    "For default parameters $\\Delta t=0.25\\text{ms}$ and $\\delta_P=0.5$, this means that a single spike reduces the pool by 1/8 of its current size.\n",
    "\n",
    "### Evolution of $P$ in the NEST implementation\n",
    "\n",
    "In NEST, we modify the equations above to obtain a definite jump in pool size on transmission of a spike, without any dependence on the integration time step (fixing explicitly $P_{\\text{peak}}$):\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\text{d}P}{\\text{d}t} &= \\frac{1-P}{\\tau_P} \\\\\n",
    "P &\\leftarrow ( 1 - \\delta_P^*) P \n",
    "\\end{align}\n",
    "\n",
    "$P$ is only updated when a spike passes the synapse, in the following way (where $\\Delta$ is the time since the last spike through the same synapse):\n",
    "\n",
    "1. Recuperation: $P\\leftarrow 1 - ( 1 - P ) \\exp( -\\Delta / \\tau_P )$\n",
    "2. Spike transmission with $w_{\\text{eff}} = P w$\n",
    "3. Depletion: $P \\leftarrow ( 1 - \\delta_P^*) P$\n",
    "\n",
    "To achieve approximately the same depletion as in Synthesis, use $\\delta_P^*=\\Delta t\\delta_p$.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests of the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize as so\n",
    "import scipy.integrate as si\n",
    "import matplotlib.pyplot as plt\n",
    "import nest\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuron Model\n",
    "\n",
    "#### Passive properties\n",
    "\n",
    "Test relaxation of neuron and threshold to equilibrium values in absence of intrinsic currents and input. We then have\n",
    "\\begin{align}\n",
    "\\tau_m \\dot{V}&= \\left[-g_{NaL}(V-E_{Na})-g_{KL}(V-E_K)\\right] = -(g_{NaL}+g_{KL})V+(g_{NaL}E_{Na}+g_{KL}E_K)\\\\\n",
    "\\Leftrightarrow\\quad \\tau_{\\text{eff}}\\dot{V} &= -V+V_{\\infty}\\\\\n",
    "V_{\\infty} &= \\frac{g_{NaL}E_{Na}+g_{KL}E_K}{g_{NaL}+g_{KL}}\\\\\n",
    "\\tau_{\\text{eff}}&=\\frac{\\tau_m}{g_{NaL}+g_{KL}}\n",
    "\\end{align}\n",
    "with solution\n",
    "\\begin{equation}\n",
    "V(t) = V_0 e^{-\\frac{t}{\\tau_{\\text{eff}}}} + V_{\\infty}\\left(1-e^{-\\frac{t}{\\tau_{\\text{eff}}}} \\right)\n",
    "\\end{equation}\n",
    "and for the threshold\n",
    "\\begin{equation}\n",
    "\\theta(t) = \\theta_0 e^{-\\frac{t}{\\tau_{\\theta}}} + \\theta_{eq}\\left(1-e^{-\\frac{t}{\\tau_{\\theta}}} \\right)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Vpass(t, V0, gNaL, ENa, gKL, EK, taum, I=0):\n",
    "    tau_eff = taum / (gNaL + gKL)\n",
    "    Vinf = (gNaL * ENa + gKL * EK + I) / (gNaL + gKL)\n",
    "    return V0 * np.exp(-t / tau_eff) + Vinf * (1 - np.exp(-t / tau_eff))\n",
    "\n",
    "\n",
    "def theta(t, th0, theq, tauth):\n",
    "    return th0 * np.exp(-t / tauth) + theq * (1 - np.exp(-t / tauth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vex  = -76.694, Vsim  = -76.694, Vex-Vsim   = -1.847e-13\n",
      "thex = -52.895, thsim = -52.895, thex-thsim = -3.553e-13\n",
      "Vex  = -70.000, Vsim  = -70.000, Vex-Vsim   = 0.000e+00\n",
      "thex = -51.000, thsim = -51.000, thex-thsim = 0.000e+00\n",
      "Vex  = -66.653, Vsim  = -66.653, Vex-Vsim   = 1.137e-13\n",
      "thex = -45.451, thsim = -45.451, thex-thsim = 1.009e-12\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.SetDefaults(\n",
    "    \"ht_neuron\", {\"g_peak_NaP\": 0.0, \"g_peak_KNa\": 0.0, \"g_peak_T\": 0.0, \"g_peak_h\": 0.0, \"tau_theta\": 10.0}\n",
    ")\n",
    "hp = nest.GetDefaults(\"ht_neuron\")\n",
    "\n",
    "V_0 = [-100.0, -70.0, -55.0]\n",
    "th_0 = [-65.0, -51.0, -10.0]\n",
    "T_sim = 20.0\n",
    "\n",
    "nrns = nest.Create(\"ht_neuron\", n=len(V_0), params={\"V_m\": V_0, \"theta\": th_0})\n",
    "\n",
    "nest.Simulate(T_sim)\n",
    "V_th_sim = nrns.get([\"V_m\", \"theta\"])\n",
    "\n",
    "for V0, th0, Vsim, thsim in zip(V_0, th_0, V_th_sim[\"V_m\"], V_th_sim[\"theta\"]):\n",
    "    Vex = Vpass(T_sim, V0, hp[\"g_NaL\"], hp[\"E_Na\"], hp[\"g_KL\"], hp[\"E_K\"], hp[\"tau_m\"])\n",
    "    thex = theta(T_sim, th0, hp[\"theta_eq\"], hp[\"tau_theta\"])\n",
    "    print(\"Vex  = {:.3f}, Vsim  = {:.3f}, Vex-Vsim   = {:.3e}\".format(Vex, Vsim, Vex - Vsim))\n",
    "    print(\"thex = {:.3f}, thsim = {:.3f}, thex-thsim = {:.3e}\".format(thex, thsim, thex - thsim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agreement is excellent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spiking without intrinsic currents or synaptic input\n",
    "\n",
    "The equations above hold for input current $I(t)$, but with\n",
    "\\begin{equation}\n",
    "V_{\\infty}(I) = \\frac{g_{NaL}E_{Na}+g_{KL}E_K}{g_{NaL}+g_{KL}} + \\frac{I}{g_{NaL}+g_{KL}}\n",
    "\\end{equation}\n",
    "In NEST, we need to inject input current into the `ht_neuron` with a `dc_generator`, whence the current will set on only at a later time and we need to take this into account. For simplicity, we assume that $V$ is initialized to $V_{\\infty}(I=0)$ and that current onset is at $t_I$. We then have for $t\\geq t_I$\n",
    "\\begin{equation}\n",
    "V(t) = V_{\\infty}(0) e^{-\\frac{t-t_I}{\\tau_{\\text{eff}}}} + V_{\\infty}(I)\\left(1-e^{-\\frac{t-t_I}{\\tau_{\\text{eff}}}} \\right)\n",
    "\\end{equation}\n",
    "If we also initialize $\\theta=\\theta_{\\text{eq}}$, the threshold is constant and we have the first spike at\n",
    "\\begin{align}\n",
    "V(t) &= \\theta_{\\text{eq}}\\\\\n",
    "\\Leftrightarrow\\quad t &= t_I -\\tau_{\\text{eff}} \\ln \\frac{\\theta_{\\text{eq}}-V_{\\infty}(I)}{V_{\\infty}(0)-V_{\\infty}(I)}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t_first_spike(gNaL, ENa, gKL, EK, taum, theq, tI, I):\n",
    "    tau_eff = taum / (gNaL + gKL)\n",
    "    Vinf0 = (gNaL * ENa + gKL * EK) / (gNaL + gKL)\n",
    "    VinfI = (gNaL * ENa + gKL * EK + I) / (gNaL + gKL)\n",
    "    return tI - tau_eff * np.log((theq - VinfI) / (Vinf0 - VinfI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tex  = 34.4056, tsim  = 34.4060, tex-tsim = -0.0004\n",
      "tex  = 10.1174, tsim  = 10.1180, tex-tsim = -0.0006\n",
      "tex  = 5.4503, tsim  = 5.4510, tex-tsim = -0.0007\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.resolution = 0.001\n",
    "nest.SetDefaults(\"ht_neuron\", {\"g_peak_NaP\": 0.0, \"g_peak_KNa\": 0.0, \"g_peak_T\": 0.0, \"g_peak_h\": 0.0})\n",
    "hp = nest.GetDefaults(\"ht_neuron\")\n",
    "\n",
    "I = [25.0, 50.0, 100.0]\n",
    "tI = 1.0\n",
    "delay = 1.0\n",
    "T_sim = 40.0\n",
    "\n",
    "nrns = nest.Create(\"ht_neuron\", n=len(I))\n",
    "dcgens = nest.Create(\"dc_generator\", n=len(I), params={\"amplitude\": I, \"start\": tI})\n",
    "srs = nest.Create(\"spike_recorder\", n=len(I))\n",
    "nest.Connect(dcgens, nrns, \"one_to_one\", {\"delay\": delay})\n",
    "nest.Connect(nrns, srs, \"one_to_one\")\n",
    "nest.Simulate(T_sim)\n",
    "\n",
    "t_first_sim = [t[0] for t in srs.get(\"events\", \"times\")]\n",
    "\n",
    "for dc, tf_sim in zip(I, t_first_sim):\n",
    "    tf_ex = t_first_spike(hp[\"g_NaL\"], hp[\"E_Na\"], hp[\"g_KL\"], hp[\"E_K\"], hp[\"tau_m\"], hp[\"theta_eq\"], tI + delay, dc)\n",
    "    print(\"tex  = {:.4f}, tsim  = {:.4f}, tex-tsim = {:.4f}\".format(tf_ex, tf_sim, tf_ex - tf_sim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agreement is as good as possible: All spikes occur in NEST at then end of the time step containing the expected spike time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inter-spike interval\n",
    "\n",
    "After each spike, $V_m = \\theta = E_{Na}$, i.e., all memory is erased. We can thus treat ISIs independently. $\\theta$ relaxes according to the equation above. For $V_m$, we have during $t_{\\text{spike}}$ after a spike\n",
    "\n",
    "\\begin{align}\n",
    "\\tau_m\\dot{V} &= {-g_{\\text{NaL}}(V-E_{\\text{Na}})\n",
    "-g_{\\text{KL}}(V-E_{\\text{K}})+I}\n",
    "-\\frac{\\tau_m}{\\tau_{\\text{spike}}}({V-E_{\\text{K}}})\\\\\n",
    "&=  -(g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}})V+(g_{NaL}E_{Na}+g_{KL}E_K+\\frac{\\tau_m}{\\tau_{\\text{spike}}}E_K)\n",
    "\\end{align}\n",
    "\n",
    "thus recovering the same for for the solution but with\n",
    "\n",
    "\\begin{align}\n",
    "\\tau^*_{\\text{eff}} &= \\frac{\\tau_m}{g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}}}\\\\\n",
    "V^*_{\\infty} &= \\frac{g_{NaL}E_{Na}+g_{KL}E_K+I+\\frac{\\tau_m}{\\tau_{\\text{spike}}}E_K}{g_{NaL}+g_{KL}+\\frac{\\tau_m}{\\tau_{\\text{spike}}}}\n",
    "\\end{align}\n",
    "\n",
    "Assuming that the ISI is longer than the refractory period $t_{\\text{spike}}$, and we had a spike at time $t_s$, then we have at $t_s+t_{\\text{spike}}$\n",
    "\n",
    "\\begin{align}\n",
    "V^* &= V(t_s+t_{\\text{spike}}) = E_{Na} e^{-\\frac{t_{\\text{spike}}}{\\tau^*_{\\text{eff}}}} + V^*_{\\infty}(I)\\left(1-e^{-\\frac{t_{\\text{spike}}}{\\tau^*_{\\text{eff}}}} \\right)\\\\\n",
    "\\theta^* &= \\theta(t_s+t_{\\text{spike}}) = E_{Na} e^{-\\frac{t_{\\text{spike}}}{\\tau_{\\theta}}} + \\theta_{eq}\\left(1-e^{-\\frac{t_{\\text{spike}}}{\\tau_{\\theta}}} \\right)\\\\\n",
    "t^* &= t_s+t_{\\text{spike}}\n",
    "\\end{align}\n",
    "\n",
    "For $t>t^*$, the normal equations apply again, i.e.,\n",
    "\\begin{align}\n",
    "V(t) &= V^* e^{-\\frac{t-t^*}{\\tau_{\\text{eff}}}} + V_{\\infty}(I)\\left(1-e^{-\\frac{t-t^*}{\\tau_{\\text{eff}}}} \\right)\\\\\n",
    "\\theta(t) &= \\theta^* e^{-\\frac{t-t^*}{\\tau_{\\theta}}} + \\theta_{\\infty}\\left(1-e^{-\\frac{t-t^*}{\\tau_{\\theta}}}\\right)\n",
    "\\end{align}\n",
    "\n",
    "The time of the next spike is then given by\n",
    "\\begin{equation}\n",
    "V(\\hat{t}) = \\theta(\\hat{t})\n",
    "\\end{equation}\n",
    "which can only be solved numerically. The ISI is then obtained as $\\hat{t}-t_s$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Vspike(tspk, gNaL, ENa, gKL, EK, taum, tauspk, I=0):\n",
    "    tau_eff = taum / (gNaL + gKL + taum / tauspk)\n",
    "    Vinf = (gNaL * ENa + gKL * EK + I + taum / tauspk * EK) / (gNaL + gKL + taum / tauspk)\n",
    "    return ENa * np.exp(-tspk / tau_eff) + Vinf * (1 - np.exp(-tspk / tau_eff))\n",
    "\n",
    "\n",
    "def thetaspike(tspk, ENa, theq, tauth):\n",
    "    return ENa * np.exp(-tspk / tauth) + theq * (1 - np.exp(-tspk / tauth))\n",
    "\n",
    "\n",
    "def Vpost(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I=0):\n",
    "    Vsp = Vspike(tspk, gNaL, ENa, gKL, EK, taum, tauspk, I)\n",
    "    return Vpass(t - tspk, Vsp, gNaL, ENa, gKL, EK, taum, I)\n",
    "\n",
    "\n",
    "def thetapost(t, tspk, ENa, theq, tauth):\n",
    "    thsp = thetaspike(tspk, ENa, theq, tauth)\n",
    "    return theta(t - tspk, thsp, theq, tauth)\n",
    "\n",
    "\n",
    "def threshold(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I, theq, tauth):\n",
    "    return Vpost(t, tspk, gNaL, ENa, gKL, EK, taum, tauspk, I) - thetapost(t, tspk, ENa, theq, tauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isi_ex  = 14.3144, isi_sim (min, mean, max)  = (14.3150, 14.3150, 14.3150)\n",
      "isi_ex  = 5.6602, isi_sim (min, mean, max)  = (5.6610, 5.6610, 5.6610)\n",
      "isi_ex  = 3.9718, isi_sim (min, mean, max)  = (3.9720, 3.9720, 3.9720)\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "nest.resolution = 0.001\n",
    "nest.SetDefaults(\"ht_neuron\", {\"g_peak_NaP\": 0.0, \"g_peak_KNa\": 0.0, \"g_peak_T\": 0.0, \"g_peak_h\": 0.0})\n",
    "hp = nest.GetDefaults(\"ht_neuron\")\n",
    "\n",
    "I = [25.0, 50.0, 100.0]\n",
    "tI = 1.0\n",
    "delay = 1.0\n",
    "T_sim = 1000.0\n",
    "\n",
    "nrns = nest.Create(\"ht_neuron\", n=len(I))\n",
    "dcgens = nest.Create(\"dc_generator\", n=len(I), params={\"amplitude\": I, \"start\": tI})\n",
    "srs = nest.Create(\"spike_recorder\", n=len(I))\n",
    "nest.Connect(dcgens, nrns, \"one_to_one\", {\"delay\": delay})\n",
    "nest.Connect(nrns, srs, \"one_to_one\")\n",
    "nest.Simulate(T_sim)\n",
    "\n",
    "isi_sim = []\n",
    "for ev in srs.events:\n",
    "    t_spk = ev[\"times\"]\n",
    "    isi = np.diff(t_spk)\n",
    "    isi_sim.append((np.min(isi), np.mean(isi), np.max(isi)))\n",
    "\n",
    "for dc, (isi_min, isi_mean, isi_max) in zip(I, isi_sim):\n",
    "    isi_ex = so.bisect(\n",
    "        threshold,\n",
    "        hp[\"t_ref\"],\n",
    "        50,\n",
    "        args=(\n",
    "            hp[\"t_ref\"],\n",
    "            hp[\"g_NaL\"],\n",
    "            hp[\"E_Na\"],\n",
    "            hp[\"g_KL\"],\n",
    "            hp[\"E_K\"],\n",
    "            hp[\"tau_m\"],\n",
    "            hp[\"tau_spike\"],\n",
    "            dc,\n",
    "            hp[\"theta_eq\"],\n",
    "            hp[\"tau_theta\"],\n",
    "        ),\n",
    "    )\n",
    "    print(\n",
    "        \"isi_ex  = {:.4f}, isi_sim (min, mean, max)  = ({:.4f}, {:.4f}, {:.4f})\".format(\n",
    "            isi_ex, isi_min, isi_mean, isi_max\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ISIs are as predicted: measured ISI is predicted rounded up to next time step\n",
    "- ISIs are perfectly regular as expected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intrinsic Currents\n",
    "\n",
    "##### Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class Channel:\n",
    "    \"\"\"\n",
    "    Base class for channel models in Python.\n",
    "    \"\"\"\n",
    "\n",
    "    def tau_m(self, V):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def tau_h(self, V):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def h_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def D_inf(self, V):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def dh(self, h, t, V):\n",
    "        return (self.h_inf(V) - h) / self.tau_h(V)\n",
    "\n",
    "    def dm(self, m, t, V):\n",
    "        return (self.m_inf(V) - m) / self.tau_m(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def voltage_clamp(channel, DT_V_seq, nest_dt=0.1):\n",
    "    \"Run voltage clamp with voltage V through intervals DT.\"\n",
    "\n",
    "    # NEST part\n",
    "    nest_g_0 = {\"g_peak_h\": 0.0, \"g_peak_T\": 0.0, \"g_peak_NaP\": 0.0, \"g_peak_KNa\": 0.0}\n",
    "    nest_g_0[channel.nest_g] = 1.0\n",
    "\n",
    "    nest.ResetKernel()\n",
    "    nest.resolution = nest_dt\n",
    "    nrn = nest.Create(\"ht_neuron\", params=nest_g_0)\n",
    "    mm = nest.Create(\"multimeter\", params={\"record_from\": [\"V_m\", \"theta\", channel.nest_I], \"interval\": nest_dt})\n",
    "    nest.Connect(mm, nrn)\n",
    "\n",
    "    # ensure we start from equilibrated state\n",
    "    nrn.set(V_m=DT_V_seq[0][1], equilibrate=True, voltage_clamp=True)\n",
    "    for DT, V in DT_V_seq:\n",
    "        nrn.set(V_m=V, voltage_clamp=True)\n",
    "        nest.Simulate(DT)\n",
    "    t_end = nest.biological_time\n",
    "\n",
    "    # simulate a little more so we get all data up to t_end to multimeter\n",
    "    nest.Simulate(2 * nest.min_delay)\n",
    "\n",
    "    tmp = pd.DataFrame(mm.events)\n",
    "    nest_res = tmp[tmp.times <= t_end]\n",
    "\n",
    "    # Control part\n",
    "    t_old = 0.0\n",
    "    try:\n",
    "        m_old = channel.m_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        m_old = None\n",
    "    try:\n",
    "        h_old = channel.h_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        h_old = None\n",
    "    try:\n",
    "        D_old = channel.D_inf(DT_V_seq[0][1])\n",
    "    except NotImplementedError:\n",
    "        D_old = None\n",
    "\n",
    "    t_all, I_all = [], []\n",
    "    if D_old is not None:\n",
    "        D_all = []\n",
    "\n",
    "    for DT, V in DT_V_seq:\n",
    "        t_loc = np.arange(0.0, DT + 0.1 * nest_dt, nest_dt)\n",
    "        I_loc = channel.compute_I(t_loc, V, m_old, h_old, D_old)\n",
    "        t_all.extend(t_old + t_loc[1:])\n",
    "        I_all.extend(I_loc[1:])\n",
    "        if D_old is not None:\n",
    "            D_all.extend(channel.D[1:])\n",
    "        m_old = channel.m[-1] if m_old is not None else None\n",
    "        h_old = channel.h[-1] if h_old is not None else None\n",
    "        D_old = channel.D[-1] if D_old is not None else None\n",
    "        t_old = t_all[-1]\n",
    "\n",
    "    if D_old is None:\n",
    "        ctrl_res = pd.DataFrame({\"times\": t_all, channel.nest_I: I_all})\n",
    "    else:\n",
    "        ctrl_res = pd.DataFrame({\"times\": t_all, channel.nest_I: I_all, \"D\": D_all})\n",
    "\n",
    "    return nest_res, ctrl_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_h channel\n",
    "\n",
    "The $I_h$ current is governed by\n",
    "\\begin{align}\n",
    "I_h &= g_{\\text{peak}, h} m_h(V, t) (V-E_h) \\\\\n",
    "\\frac{\\text{d}m_h}{\\text{d}t} &= \\frac{m_h^{\\infty}-m_h}{\\tau_{m,h}(V)}\\\\\n",
    "m_h^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(\\frac{V+75\\text{mV}}{5.5\\text{mV}}\\right)} \\\\\n",
    "\\tau_{m,h}(V) &= \\frac{1}{\\exp(-14.59-0.086V) + \\exp(-1.87  + 0.0701V)}\n",
    "\\end{align}\n",
    "\n",
    "We first inspect $m_h^{\\infty}(V)$ and $\\tau_{m,h}(V)$ to prepare for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class Ih(Channel):\n",
    "    nest_g = \"g_peak_h\"\n",
    "    nest_I = \"I_h\"\n",
    "\n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "\n",
    "    def tau_m(self, V):\n",
    "        return 1 / (np.exp(-14.59 - 0.086 * V) + np.exp(-1.87 + 0.0701 * V))\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1 / (1 + np.exp((V + 75) / 5.5))\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.m = si.odeint(self.dm, m0, t, args=(V,))\n",
    "        return -self.hp[\"g_peak_h\"] * self.m * (V - self.hp[\"E_rev_h\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+z4hIW//37YDzgddFxJ5kDODrCPzI5BUs357MC5d3KlYCABAgATx1zlO8OfBNftjwA/3e7ce2lG0lFG3Z69Awjleu6Mzy7cn867NlVOZhgY0xxpjySkTOF5G1IpIoIg/k832siHwtIktFZKWIXH/C8yXIU8Bs4P+Af/mXfzqNx2k7h9uA+0TIAnLwdUDQYo4QFASEi0gOvhqAHcCDwBn+78cDPwP3A4OBiaqaBWwSkUSgBzCnGNc3lcSEP7YyacE27jirJee2rVNi5x3VdRSNYxsz7NNhnP7e6cy6bhYNYxqW2PnL0jlt6/Cv/m14Ztpa2tSJ4vazWrkdkjHGGFNl+F9evwacC2wD5ovIFFVdlWe324BVqjpIRGoBa0XkI1XNPs5pLwHaaLxmHef7E3JUE+AfEjRAlfCSGCJUVbcDzwFbgJ1Asqr+ANRR1Z3+fXYCtf2HNAC25jnFNn/Z34jIKBFZICILPB5PUUM0FcSSrQeJn7KC01rX4u5zWpf4+c9veT7Tr51OUnoSZ44/kx2pO0r8GmXlltNbcHGn+jz3wzq+X7nL7XCMMcaYqqQHkKiqG/0P9RPxveTOS4Fo8Y1KEgXsB070MLsRCC5qQCdMAkSoW9AJnOzz92OkGr4fvBlQH4gUkatPdEg+Zfm2aVDVt1S1m6p2CwqquB06TcGSM3K4/eNF1I4O4+XhnQgMKJ2RfHo06MG0q6ax69Auzhx/JjtTK+bY+yLCU0M60LFRHPd8soTVO22+P2OMMaaMOHmh/SpwMr7WMcuBu1T1RMPzpwNLJEHelAR5+fDiNKCCagK+dXAOJ/sc6xxgk6ruVdUc4AugD7BbROoB+Nd7/PtvAxrlOb4hvhtkqihV5YHPl7ErOZNXruxMXERIqV6vd6PefHfVd2xP2c7Z75/N7kO7S/V6pSUsOJD/XdOV6LAgbhy/gKRDRapBNMYYY8xfBR1uieJfRh3zvZMX2v2BJfhekHcCXhWRE7W8mYJvCP/fgYV5FkdOODqQCLnAiYbXESBFNf+mOcc/r/QE3gG6AxnAe/jGOW0M7FPVp/wdJqqr6n0i0g74GF9VSn18nYZbqWruia5jowNVXh/O/ZP/+2oFD1xwEqNPb1Fm1/3lz1+44KMLaBbXjJkjZlIrslaZXbskLdt2kMvGzqFDw1g+urEXIUFOxwgwxhhjzLEKGh1IRHoDj6pqf//nBwFU9ck8+3wDPKWqv/o//wQ8oKp/lErMbo0UIiIJwOX42jotxjdcaBQwCV8ysAW4TFX3+/d/GBjp3/9uVf2uoGtYElA5rd6ZwuDXZtOreQ3eu647AaXUDOh4Zm6ayYUfX0jL6i35acRP1IyoWabXLymTl2znrolLuLxbI54ackqFnhjNGGOMcZODJCAIWAecDWwH5gNXqurKPPu8AexW1UdFpA6wCOioqkl/OVeCTNJ4HSYJspx8msdrvHZwFHNlHi7QkoDKJz3bw6BXfiMl08N3d/WjZlSoK3FM3zidQRMG0a5WO2ZdN4vIkDIfGrhEPPv9Gl6buYHHB7fjmt5N3Q7HGGOMqZCczBMgIgOAl/ANjf+Oqj4hIqMBVHWsiNTH1zqmHr7WNk+p6od/O0+C1NN43SkJ0iS/62i8/ukoZksCTEXyr0+X8tmibXx4Q0/6tnT3DfzUdVMZPHEwg1oP4vNhnxMYUPGmrvB6lRvfX8Av6/by8U296NGsutshGWOMMRWOW5OFFYc1BDYVxleLt/Ppwm3cfmZL1xMAgIGtB/JS/5eYvHYy9/14n9vhFElAgPDi5Z1oVD2CWz9ayM7kDLdDMsYYY4wDkiDXFed4x0mACNVE6CBCl8NLcS5sTGFsSkrj4S+X06Npde46u/xMdHVHzzu4o8cdvDD3Bd6Y/4bb4RRJbHgwb13TlYzsXEZ/sJDMnBP2tzfGGGOMyyRBHsE32maROUoCRHgcWAa8DDzvX54rzoWNcSrb4+XOCYsJDgrgpeGdCAosXxVYL/Z/kQtbXcgd393BtMRpbodTJK3qRPPC5Z1Yui2Z//tqBZW5maAxxhhTkUmCvIVvPoFri3Mep09Tw4AWqpyhypn+5aziXNgYp57/cS3Ltyfz9JAO1I8LdzucvwkMCGTCkAm0r92eYZ8OY9nuZW6HVCT929XlzrNb8dnCbbw/x1GfImOMMcaUvSuBxzX+hBOJFchpErACiCvOhYwpit/WJ/HmrI1c1bMx/dsVenLqMhMdGs3UK6cSHRrNwI8HVthZhe8+uxXnnFybx6euYt7GfW6HY4wxxpi/GwhMkgQp1kRJjkYHEqEbMBlfMnBkilFVLirOxUubjQ5Use1Py+b8l34hJjyYr28/lfCQ8j/6zuKdi+n3bj9OrnUyv17/K2FBYW6HVGgpmTlc/NpsktNz+PqOU8tl7YsxxhhTnpT16ECSIO2B/2m89i7qOZzWBIwHngae4mifgOeLelFjCqKq3PfZMg6m5zBmeKcKkQAAdK7XmQ8v/ZAFOxZwz7R73A6nSGLCgnnrmm5kebyM/tA6ChtjjDHljcbrCmBIcc7hNAlIUuVlVWaqMuvwUpwLG3MiH83bwvTVu7nv/Da0qx/rdjiFcvFJF3Nfn/sYu3AsHy37yO1wiqRl7ShevLwTy6yjsDHGGFMuabzuKM7xTpsDvYCvGdAU/tocaFFxLl7arDlQxbR+dyoDX/mNns1r8N513QkIELdDKjSP18NZ489i4c6FzL9pPm1rtXU7pCJ58cd1jJmxnoSL2jGiT1O3wzHGGGPKJTcmC5MEqQXcBDQFgg6Xa7yOdHJ8UMG7ANDZv+6Vp0zBRggyJSszJ5c7JiwmKjSI5y7rUCETAICggCAmDp1I5zc7M3TSUP646Q+iQqLcDqvQ7jq7FSt3JPP41FWcVDeans1ruB2SMcYYY3wmA78C04FCt911VBNQ4EmEEaqML/aJSpjVBFQ8j329indmb+Kd67px1kl13A6n2H7a9BPnfnAul7e7nI8u/QiRipfUpGTmcPGrs0nJzGHK7dZR2BhjjDmWSzUBSzReOxX1+JKademuEjqPqcJmrt3DO7M3MaJ3k0qRAACc1ewsHjvjMSasmMDYBWPdDqdIYsKCeevarmTmeLnFOgobY4wx5cVUSZABRT24pGoCFqseaTJUblhNQMWxNzWLC8b8Qo3IUCbf3pew4IoxGpATXvUy8OOBzNg0g9kjZ9Otfje3QyqSH1buYtQHC7msa0OeGdqhQtZqGGOMMaWhLGsCJEFS8TXLFyASX3/dHP9n1XiNcXSeEkoCFqnSpdgnKmGWBFQMqsr1781nzoZ9TLn9VNrUjXY7pBK3L30fXd7qQoAEsOTmJcSGVawRjw574cd1vDxjPY8Oast1fZu5HY4xxhhTLrjRHKggkiDtNF5XHu/7kmoOZK8ETZG99/tmfl67l/+78ORKmQAA1IiowSdDP2Fr8lZu/+52t8MpMt+MwnV4/JvV/L4hye1wjDHGGHN8H5zoy5JKAmaX0HlMFbN6ZwpPfruGc06uzdW9mrgdTqnq1bAX/z7t33y47EMmrZzkdjhFEhAgvHh5R5rXjOS2jxaxdX+62yEZY4wxJn8nfEnvdJ6AR/IrV+WxIgZVJqw5UPmWkZ3LRa/+xsGMHKbd1Y8aUaFuh1TqPF4Pp75zKuv2rWPZLctoGNPQ7ZCKZHNSGhe9+hv148L5/JY+RIY6HW3YGGOMqXzKaXOgRRqvx22u77QmIC3PkgtcgG9iAmOK7IlvV7F+zyFeGNaxSiQA4Js/4INLPiArN4vrvroOr3rdDqlImtaM5NUru7Budyr//HQpXq/NKGyMMcZUJI6SAFWez7M8AZwBNCjVyEyl9sPKXXw4dwujTmtOv1a13A6nTLWq0YoX+7/IjE0zeHney26HU2Snta7FQwNO5rsVu3h1ZqLb4RhjjDHmr7JP9GWRRgcSoRrwhyqtihpVWbDmQOXTzuQMBoz5lQbVwvnilr6EBJVU15SKQ1UZPHEwP2z4gQWjFtC+dnu3QyoSVeUfk5byxeLtvHVNV85rV9ftkIwxxpgy51ZzIEmQDvha5xxpl6vx+oWTYx09fYmwXIRl/mUlsBYYU4RY85xT4kTkMxFZIyKrRaS3iFQXkR9FZL1/XS3P/g+KSKKIrBWR/sW5tnGPJ9fLXROXkOXx8vLwzlUyAQAQEcZdNI7YsFiu/uJqsjxZbodUJCLCfy89hY4NY7nnkyWs3ZXqdkjGGGNMlSAJ8g7wDjAEGORfBjo+3mHH4LzDtniA3ap4ChfqseeU8cCvqjpOREKACOAhYL+qPiUiDwDVVPV+EWkLTAB6APWB6UBrVT3h1KVWE1D+vPjjOsbMWM8LwzpyaZeK2Sm2JE1dN5VBEwbxrz7/4plzn3E7nCLblZzJRa/+RkhQAF/d1peaVaSPhzHGGAPOagJE5Hx8L9EDgXGq+lQ++5wBvAQEA0mqevpxz5cgqzRe2xY1Zqd9Av5U5U8gA1/g9UVoXNSLikgMcBrwtu/8mq2qB4HBwHj/buOBi/3bg4GJqpqlqpuARHwJgalA5mzYxys/rWdIl4aWAPgNbD2QUV1G8dzvz/Hrn7+6HU6R1Y0N43/XdmNvahY3f7CQzJwT5ufGGGNMlSIigcBr+AbXaQtc4X/JnXefOOB14CJVbQdcVsBp50iClG4SIMJFIqwHNgGzgM3Ad0W9KNAc2Au8KyKLRWSciEQCdVR1J4B/Xdu/fwNga57jt3GcjskiMkpEFojIAo+nWJUVpgTtO5TF3Z8spmmNSB4b3M7tcMqV5/s/T9O4poycMpL0nIo77n7HRnG8MKwTC/88wINfLKckZiM3xhhjKokeQKKqblTVbGAivpfceV0JfKGqWwBUdU8B5xyPLxFYKwmyTBJkuSTIMqcBOW2Q/TjQC1inSjPgbIo3QVgQ0AV4Q1U74xt69IET7J/fZAf5PmGo6luq2k1VuwUF2djl5YHXq/zz06UcSM/hlSs725jyx4gKieLti94mcX8i//fT/7kdTrFc2KEe/zi3NV8u3s7rP29wOxxjjDGmrAQdfgntX0Yd872TF9qtgWoi8rOILBSRawu45jvANcD5HO0PMMhxwA73y1FlnwgBIgSoMlOEp51eJB/bgG2qOs//+TN8ScBuEamnqjtFpB6wJ8/+jfIc3xDYUYzrmzL0zuxNzFy7l8cGt6Nd/Vi3wymXzmx2JqO7jualuS8xtO1Q+jTq43ZIRXb7WS3ZsPcQz36/lmY1IxlwSj23QzLGGGNKm0dVu53geycvtIOArvhetocDc0RkrqquO845t2i8Til8qEcv5sRBEaKAX4CPRNgDRe8YrKq7RGSriLRR1bX4fthV/mUE8JR/Pdl/yBTgYxF5AV/H4FbAH0W9vik7S7ce5Olpa+jfrg7X9GpS8AFV2DPnPsO3id8ycvJIFt+8mPDgcLdDKhIR4akhHdiyP517Jy2hYbVwOjSMczssY4wxxk1OXmhvw9cZOA1IE5FfgI7A8ZKANZIgHwNfA0eGGSzRIULxtVlKB+4BpgEbKMQQRMdxB/CRiCwDOgH/xffwf66IrAfO9X9GVVcCk/AlCdOA2woaGci4LzkjhzsmLKZ2dBjPDOmISH5JsDksOjSacYPGsXbfWuJ/jnc7nGIJCw7krWu7USMylBvHL2BncobbIRljjDFumg+0EpFm/lExh+N7yZ3XZKCfiASJSATQE1h9gnOG43v4P49SHCL0aVXuL6isvLEhQt2jqtz8wUJ+WrOHT27uRdcm1d0OqcIY9fUo3l78Nr+P/J2eDXu6HU6xrN2VypA3fqdhtXA+Hd2b6LBgt0MyxhhjSpzDIUIH4Bv+MxB4R1WfEJHRAKo61r/Pv4DrAS++YURfKrWYHSYBi1TpckzZMlU6lFZgJcGSAPe8OWsDT363hn8PbMsNpzZzO5wKJTkzmfZvtCc6JJpFNy8iLCjM7ZCK5Zd1exn53nx6Na/BO9d1r7ITxBljjKm83JoxuDhO+NdYhFtEWA60yTNj8DIRNgGOhyAyVcu8jft45vu1DDilLiP7NnU7nAonNiyW/w36H6uTVvPYrMfcDqfYTmtdiycvPYXfEpN44ItlNnSoMcYYUw6csCZAhFigGvAkfx3CM1WV/aUcW7FZTUDZ25OayYUv/0Z0aBCTb+9rzT+KYeTkkby/9H3m3jiXbvVPNOBAxTBm+npenL6OO85qyT/Oa+N2OMYYY0yJqYg1AY6aA1VUlgSULU+ul6vGzWPptoN8dVtfTqob43ZIFdrBzIO0fa0ttSJrseCmBQQHVuyESlV54PPlfLJgK/+95BSu7FnkSceNMcaYcsWNJEASpA6+gXXqa7xe4J89uLfG69tOjrfGuabEPPfDOuZt2s9/LznFEoASEBcWx+sXvs6y3ct49vdn3Q6n2ESE/1zSnjPa1OL/vlrOjNW73Q7JGGOMqcjeA77HN3w++IYSvdvpwZYEmBLx46rdjJ21gSt6NObSLg3dDqfSuPikixnadiiPzXqMNUlr3A6n2IIDA3jtyi60rR/D7R8vZunWg26HZIwxxlRUNTVeJ+EbSQiNVw/geAh9R0lAfrMDF3PGYFOJbE5K495JS2jfIIb4QW3dDqfSeeWCV4gIjuCmr2/Cq163wym2yNAg3rmuOzWiQrj+vfkk7jnkdkjGGGNMRZQmCVID/8zDkiC9gGSnBzutCTg3n7ILnF7EVF6pmTnc9P4CAgOEN67qSlhwoNshVTp1o+ryQv8X+G3Lb4xdMNbtcEpE7egwPrihJwEiXPP2PLYdSHc7JGOMMaaiuRffhGMtJEFmA+8Ddzo92IYINUXm9Sr3fLKUjUlpvH5lFxpVj3A7pEprRMcRnNv8XO6ffj9bkre4HU6JaFYzkvdH9iAty8PV4+axNzWr4IOMMcYYc9hK4HSgD3Az0A5w3Ha4oJqAj/FNQTyFo9MRDwK6qnJ1UaI1lcdL09cxffVu/n3hyfRpWdPtcCo1EeHNgW/iVS+3fHNLpRlrv239GN69vju7U7K49p0/SM7IcTskY4wxpqKYo/Hq0XhdqfG6QuM1B5jj9GDHQ4SKEAjUAYIOl6lSrl9J2hChpeebZTu57eNFDOvWkKeHdEBE3A6pSnhp7kvc8/09fHTpR1x5ypVuh1Niflm3lxvGz6dDwzg+uKEHESFBBR9kjDHGlBNlOUSoJEhdoAHwIXAlcPghLAYYq/F6kqPzOEkCRLgdeBTYjb8HMr5hvzsULuyyZUlA6Vi1I4Uhb/zOyfWimTCqF6FB1g+grOR6c+n7Tl82HNjA6ttWUzOi8tTAfLfcl1j2a1WL/13bjZAgG7zMGGNMxVDGScAI4DqgG7Agz1epwHsar184Oo/DJCAR6KnKvsKH6h5LAkre/rRsBr3yG7leZcodfakdHeZ2SFXOij0r6PJmFy5rdxkfXfqR2+GUqEnzt3Lf58sYcEpdXh7emaBASwSMMcaUfy5NFjZE4/Xzoh7vtM59K4UYcshUTjm5Xm79aCFJh7L4dHRvSwBc0r52ex7u9zCPznqUK9pfwcDWA90OqcQM696I1CwPj09dhcgSxlzeyRIBY4wxJh8ar59LglyIr0NwWJ7yx5wc7zQJ2Aj8LMI3wJEhPFR5oRCxmgpMVfn3VyuYu3E/L13eiQ4N49wOqUp7sN+DfLb6M0ZPHc3KW1cSGxbrdkgl5oZTm+H1Kk98uxoBXrJEwBhjjPkbSZCxQARwJjAOGAr84fR4p39ZtwA/AiFAdJ7FVBGvzUxk4vyt3HlWSy7u3MDtcKq8kMAQ3rnoHXYe2sl9P97ndjgl7qbTmvPQgJOYumwn90xaiie34k+SZowxxpSwPhqv1wIHNF4TgN5AI6cHO6oJUCWhiMGZSuDLxdt47od1XNq5Afec29rtcIxf9wbdubfXvTw35zmGtx/Omc3OdDukEjXqtBaowpPf+YY8fnFYR6sRMMYYY47K8K/TJUHqA/uAZk4PdpQEiFALuI9j2xwpZzmP01REv29I4r7PltG7eQ2esqFAy52EMxP4au1X3Pj1jSy/ZTkRwZVrwrabT2+BAk99twYBXrBEwBhjjDlsqiRIHPAssAhQfM2CHHH61/QjfDOQNQMSgM3A/MJEaSqedbtTufmDhTSrGcnYa7rakI3lUERwBOMGjWPjgY38+6d/ux1OqRh9egvuP/8kpizdwb2TlpJjTYOMMcYYgGc0Xg/6RwhqApwE/MfpwU6f6mqo8jaQo8osVUYCvQofq6ko9qRkcv278wkPDuTd63sQGx7sdkjmOE5vejqju47mpXkvMW/bPLfDKRW3nHE0Ebjlw0Vk5uS6HZIxxhjjtiOzA2u8Zmm8JlOIGYOdjg6U41/vFOFCYAfQ0HGIpkJJy/Jw/XvzOZCezaSbe9MgLtztkEwBnj73aaaun8rIKSNZNGoRoUGhbodU4m45owWRoYE8Mnkl1787n/+N6EZUqM0sbIwxpmrJM2NwuCRIZ/46Y7DjdsFOawL+I0Is8A/gn/jaG93tONrjEJFAEVksIlP9n6uLyI8ist6/rpZn3wdFJFFE1opI/+Je2+QvMyeXmz9YyJpdqbx2VRfaN6g8Q09WZjGhMYy9cCyr9q7iiV+fcDucUnNt76a8dHkn/ti8n6v+N5cDadluh2SMMcY4IiLn+59jE0XkgRPs111EckVk6HF26Q88h++F/PN5lnuBhxzH43DG4L6qzC6orLBE5F58Ux7HqOpAEXkG2K+qT/lvTjVVvV9E2gITgB5AfWA60FpVT9gmwGYMLhzfZGCL+HHVbp67rCNDu1plT0Vz7ZfX8vHyj5l34zy61u/qdjilZvqq3dz28SIaV4/ggxt6UjfWJq4zxhjjnoJmDBaRQGAdcC6wDV/f2itUdVU++/0IZALvqOpnxz1nMWcMdloT8IrDMsdEpCFwIX/txTwYGO/fHg9cnKd8oqpmqeomIBFfQmBKiNer/PPTpfy4ajePDW5nCUAFNeb8MdSJqsO1X11LpifT7XBKzTlt6zB+ZA92JmcydOzv/LnPkn1jjDHlWg8gUVU3qmo2MBHf8+2x7gA+B/Y4OGdDSZAYSRCRBBknCbJIEuQ8pwGdMAkQobcI/wBqiXBvnuVRINDpRY7jJXzDjuYd6qOOqu4E8K9r+8sbAFvz7LfNX5ZPzDJKRBaIyAKPx1PMEKsGVeX/Jq9g8pId3Hd+G67t3dTtkEwRVQuvxtsXvc2qvauInxnvdjilqlfzGky4qRdpWR6Gjp3D8m3JbodkjDHGHE+Bz7Ii0gC4BBjr8JwjNV5TgPPwPTNfDzzlNKCCagJCgCh8HYjzzhScgm9q4iIRkYHAHlVd6PSQfMrybcekqm+pajdV7RYUZJ0GC6Kq/Pfb1Xw8bwu3ntGCW89o6XZIppjOb3k+N3W5iWd/f5bft/7udjil6pSGsXw6ujchgQEMe3MO01ftdjskY4wxVVPQ4ZfQ/mXUMd87eZZ9Cbi/oObu+ZxzAPCuxuvS41wn/4Md9gloosqf/u0AIEqVFKcX+fv55EngGsCDb/KxGOALoDtwhqruFJF6wM+q2kZEHgRQ1Sf9x38PPKqqJxwGyfoEFGzM9PW8OH0dI3o34dGL2tlkYJVEalYqHcZ2ICggiCU3LyEy5LjNFCuFPamZ3Dh+ASu2J/PIwLZc19fxhInGGGNMsTnoE9Ab37Nrf//nvzzb+ss2cfQhviaQDoxS1a/yPWeCvIuvNqEZ0BFfK52fNV4ddQp02ifgSRFiRIgEVgFrRfiXw2P/RlUfVNWGqtoUGA78pKpXA1OAEf7dRgCT/dtTgOEiEioizYBWwB9Fvb7x+d8vG3lx+jqGdGlI/CBLACqT6NBo3h38Lon7E3lwxoNuh1PqakeHMXFUL84+uQ6Pfr2KhK9Xkust+AWHMcYYU0bmA61EpJmIhOB7/p2SdwdVbaaqTf3Px58Btx4vAfC7AXgA6K7xmo6vBc/1TgNymgS09b/5vxj4FmiM701+SXsKOFdE1uPrPf0UgKquBCbhS0CmAbcVoqrE5OO1mYk88e1qLjylHk8POYWAAEsAKpszmp7BnT3u5JU/XmHmppluh1PqIkKCGHt1V0b2bca7szcz+sOFpGdbvyBjjDHuU1UPcDvwPbAamKSqK0VktIiMLtI549Wr8bpI4/WgJMijGq/7NF6XOT3eaXOglUAn4GPgVVVmibBUlY5FCbqsWHOgv1NVXvxxHS//lMjFnerz3GUdCQp0mguaiiY9J51OYzuRnZvNsluWERMa43ZIZeK92Zt4bOoq2tWPZdyIbtSJsSFEjTHGlJ6CmgOV+vUTZJHGa5fCHOP06e9NYDMQCfwiQhMoep8A4w5V5anv1vDyT4lc3q0Rzw/rZAlAJRcRHMH4i8ezNWUr935/r9vhlJnr+jbjrWu6sWHvIQa+8hsLNu93OyRjjDGmNBW6SYejJ0BVXlalgSoDVFF/J+EzCx2ecY3XqyR8vYo3f9nINb2a8OSlpxBoTYCqhN6NenN/3/t5e/HbfLryU7fDKTPntK3Dl7f2JSIkkOFvzeWDOZtxUvNpjDHGVASSIO3zfCz0DKFOmwOFAkOApviGCwVAlccKe8GyZM2BfLxe5eGvljPhj63ceGozHr7wZOsEXMXk5ObQ791+rElaw9LRS2kS18TtkMpMcnoOd3+ymJlr93JZ14Y8fnF7woKLO82JMcYYc5QbzYEkQX7D1xn4PeBjjdeDhTneaVuQyfhmNfMAaXkWU85le7z849OlTPhjK7ef2dISgCoqODCYCUMmoChXfnElHm/V6TAbGxHM2yO6c+dZLfl04TYuf3MOOw5muB2WMcYYUywar6cCVwGNgAWSIB9Lgpzr9HinNQErVGlf4I7lTFWvCUjJzOHWDxfxW2IS/+rfhtvOtInAqrqJKyZyxedX8HC/h/nPWf9xO5wy9/3KXfxj0lJCgwJ4+YrO9G1Z0+2QjDHGVAJudgyWBAnEN4Lny/j67ArwkMbrFyc6zmlNwO8inFKsCE2Z2pWcybCxc5i7cR/PXdbREgADwPD2wxnZaST//fW/VWLY0GP1b1eXr27rS7XIEK5+ex7Pfr+GnFyv22EZY4wxhSYJ0kES5EV8Q46eBQzSeD3Zv/1igcc7rAlYBbQENgFZ+DIMVaVDMWIvdVW1JmDNrhSuf3c+qZke3ri6C/1a1XI7JFOOpGWn0fWtrqRmp7J09FJqRlS9t+Hp2R4SpqzikwVb6dI4jjHDO9OoeoTbYRljjKmgXOoT8AswDvhU4zXjmO+u0Xj94ITHO0wC8u1F6B8lqNyqiknA74lJ3PzBQiJCA3n3uh60rV81xoU3hbNk1xJ6juvJeS3OY8rwKVW2n8jXS3fw0BfLQeCpSztwYYd6bodkjDGmAnJ7noCicJQEAIjQEejn//irKktLLaoSUtWSgC8Xb+O+z5bRrGYk713fg/px4W6HZMqxl+e9zF3T7mLM+WO4s+edbofjmi370rlz4mKWbD3IFT0a88jAtoSH2OhBxhhjnCvLJEASZDlw3Ad4jVdHLXWc1gTcBdwEHO5gcAnwliqvOLmIW6pKEuDJ9fLs92t585eN9GpenTev6UZseLDbYZlyTlUZPHEw0xKn8fN1P9OnUR+3Q3JNTq6X539Yx9hZG2heM5JnL+tI1ybV3A7LGGNMBVHGScDhFjq3+deHm/1cBaRrvDoawt9pErAM6K3qGxZUhEhgjvUJcN+BtGzumLCY3xKTuKZXE/49sC0hQTYLsHFmf8Z+evyvB2k5aSy4aQENYhq4HZKrZicmcd9ny9iZnMGN/Zpz77mtbU4BY4wxBXKpT8Bsjde+BZUdj9OnRQFy83zOpQjTE5uStWJ7MoNe/Y0/Nu/nmaEdePzi9pYAmEKpHl6dr4Z/RWpWKpdOupRMT6bbIbmqb8uaTLu7H5d3b8xbv2xkwMu/smjLAbfDMsYYY/ITKQly6uEPkiB9AMeJiNOagHuBEcCX/qKLgfdUeakwkZa1ylwT8OXibTzw+XKqR4Yw9uqudGwU53ZIpgL7YvUXDJk0hJGdRjLuonFVtqNwXr+u38v9ny1jV0omN53WnHvOsVoBY4wx+XOpJqAr8A4Q6y86CIzUeF3k6PhCdAzuApyKrwbgF1UWFzraMlYZk4Bsj5cnv1vNu7M307NZdV67qgs1o0LdDstUAo/MfITHf3mcVy54hdt73O52OOVCamYOT3yzmonzt9K8ZiSPX9zeJhgzxhjzNy5PFhYDiMZrcqGOc1gT0AtYqUqq/3M00FaVeUUJtqxUtiRg495D3DVxCcu3J3Ndn6Y8fOHJBAda8x9TMrzq5eKJF/Pt+m+Zfu10zmh6htshlRu/rNvLvyev4M996VzUsT7/d+HJ1I4JczssY4wx5YRbSYAkyIVAO+DIH6WS7hi8GOii6huOSIQAYIEqXYoUcRmpLEmAqvLpgm3ET1lJaHAATw/pQP92dd0Oy1RCKVkp9BzXk6T0JBbctIAmcflOEVIlZebk8vrPGxj78wZCgwL4x3mtuaZ3UwIDrOmUMcZUdS41BxoLRABn4ps0bCjwh8brDU6Od9wx+HACAKCKFwgqZKymCJLTc7jt40Xc9/kyOjWKY9pdp1kCYEpNTGgMX13+Fdm52VzyySUcyj7kdkjlRlhwIPee25rv7zmNTo3jePTrVVz06m8sto7Dxhhj3NFH4/Va4IDGawLQG2jk9GCnScBGEe4UIdi/3AVsLEKwphDmbdzHBWN+4YeVu7n//JP48Mae1I21JgimdLWp2YYJQyawdPdShkwaQnZuttshlSvNakby/sgevHplZ/amZnHJ679z18TFbDuQ7nZoxhhjqpYM/zpdEqQ+kAM0c3qw0yRgNNAH2A5sA3oCowoRpCmE9GwPj329iuH/m0tIUACf39KHW85oYc0OTJkZ0GoA4waN44cNPzDiqxF41et2SOWKiDCwQ31m/ON0bjuzBdNW7OKs52fx9LQ1pGTmuB2eMcaYqmGqJEgc8CywCNgMTHR6sOPRgSqiitgn4Jd1e3noy+VsO5DBVT0b89CAk4kMtZZXxh3PzH6G+6ffz+3db+flC162oUOPY/vBDJ77fi1fLt5OjcgQ7j6nFVf0aEyQddw3xpgqwc3RgQAkQUKBsMKMEGRJQDlxMD2bx6eu5vNF22heM5KnhnSgR7PqbodlDP/64V88N+c5Es5I4JHTH3E7nHJt2baD/Oeb1fyxaT/Na0VyzzmtufCUegRYLZ4xxlRqLnUMjgD+ATTWeL1JEqQV0EbjdaqT4115TSUijURkpoisFpGVInKXv7y6iPwoIuv962p5jnlQRBJFZK2I9Hcj7tKgqkxdtoNzXpjF5CXbue3MFnx7Vz9LAEy58cy5zzCi4wjif47njflvuB1OudahYRyfjOrFW9d0JVCEOyYs5oIxv/Ld8p14vZX3hYsxxpiCicj5/ufYRBF5IJ/vrxKRZf7ldxHpWMAp3wWy8HUIBl+T/f84jseNmgARqQfUU9VFIhINLMQ3C/F1wH5Vfcp/c6qp6v0i0haYAPQA6gPTgdaqmnui65T3moD1u1N5bOoqfl2fxCkNYnl6SAfa1o9xOyxj/sbj9XDJJ5fwzbpvmDh0IsPaDXM7pHIv16t8s3wnL01fx8a9abStF8M957bmnJNrW7MqY4ypZAqqCRCRQGAdcC6+h/X5wBWquirPPn2A1ap6QEQuAB5V1Z7HPWeCLNB47SYJsljjtbO/bKnGa0HJA+CwJkCEOiK8LcJ3/s9tRXA0Bml+VHWnqm9KY1VNBVYDDYDBwHj/buPxJQb4yyeqapaqbgIS8SUEFdLB9GwenbKS88f8ypKtB/n3wLZ8eWsfSwBMuRUUEMSkoZPo27gvV39xNVPWTnE7pHIvMEC4qGN9frzndF4Y1pG0bA83vb+Ai16dzXfLd5JrNQPGGFOV9AASVXWjqmbj68A7OO8Oqvq7qh4ed3ou0LCAc2ZLgoSDfx6vBGmBr2bAEafNgd4Dvsf3Fh58mczdTi9yIiLSFOgMzAPqqOpO8CUKQG3/bg2ArXkO2+Yvy+98o0RkgYgs8Hg8JRFiifHkevlgzmbOeO5n3p+zmeHdG/HzP8/ghlObWQdCU+6FB4fz9RVf07leZy795FI+WvaR2yFVCIEBwqVdGjLj3tN5ZmgHUjJzuOWjRZzzwiw+nreFzJwTVmgaY4ypHBw/y/rdAL6X7yfwKDANaCQJ8hEwA7jfaUBOh52pqcokER4EUMUjQrH/colIFPA5cLeqppygijy/L/J9jaaqbwFvga85UHFjLAmqyqx1e3ny2zWs3Z1K7+Y1eGRQW06uZ2/+TcUSFxbH9GumM3jiYK758hqSs5K5tfutbodVIQQFBjCsWyOGdGnItBW7GDtrAw99uZwXflzH9X2bcnWvJsSGB7sdpjHGmKIJEpEFeT6/5X8mPczxs6yInIkvCTj1RBfUeP1BEmQh0Mt//rs0XpMcB+xwvzQRahwOVoRegOMhiPIjIsH4EoCPVPULf/FuEamnqjv9/Qb2+Mu38dcZ0BoCO4pz/bIyZ8M+nv9hLQv+PECj6uGMvboL/dvVtTbBpsKKDo3m26u+Zdinw7jt29tIyUrhgVP/1r/JHEdggHBhh3oMOKUuczbuY+ysjTz7/Vpen5nI0K4NuaZ3U1rWjnI7TGOMMYXjUdVuJ/je0bOsiHQAxgEXqOq+E11QEmSGxuvZwDf5lBXIUcdgEboArwDtgRVALWCoKsucXOTv5xPB1+Z/v6renaf8WWBfno7B1VX1PhFpB3zM0Y7BM4BW5blj8MI/D/DCj2uZnbiPOjGh3HFWK4Z1a0RIkDX7MZVDTm4O102+jo+Xf8z9fe/nybOftOS2iFbtSGHcrxuZumwn2ble+raswbW9m3L2SbWtqaAxxlQADjoGB+FrTn82vsl35wNXqurKPPs0Bn4CrlXV3497rgQJAyKAmcAZHK1liAG+03g92VHMTkcHEiEIaOO/0FpVijwtpoicCvwKLAcOT0X6EL5+AZOAxsAW4DJV3e8/5mFgJODB13yooHZSriQBK7Yn88KP6/hpzR5qRIZw65ktuapnY8KCA8s0DmPKgle93PbNbYxdOJbRXUfz2oWvESD20FpUSYey+GT+Vj6a+yc7kjOpHxvGVb2aMKxbI2pFh7odnjHGmONwMk+AiAwAXgICgXdU9QkRGQ2gqmNFZBwwBPjTf0i+tQuSIHfh65tbH19CcTgJSAH+p/H6qqOYHdYEBAIXAk3J04RIlRecXMQtbiQBV/5vLit3pHDz6c0Z0bupzfZrKj1V5cEZD/L07KcZ2HogH1zyAXFhcW6HVaF5cr3MWLOH9+dsZnbiPgIDhDPb1GZYt4aceVJtgq12wBhjyhWXJgu7Q+P1lSIf7zAJ+BbI5K9v7lEloagXLgtuJAFb96cTGxFMTJh18DNVy2t/vMbd399Ns7hmfHn5l7Sr3c7tkCqFxD2H+HThVr5YtJ29qVnUjArhks4NGNatEa3qRLsdnjHGGMo2CZAE6Q5s1Xjd5f98LUdrEB7VeF8rmgLP4zAJWKZKh2LE64ryPlmYMZXNr3/+ymWfXsah7EO8O/hdLmt3mdshVRqeXC+z1u1l0oKtzFi9B49XOaVBLBd1rM/AjvWoFxvudojGGFNllXESsAg4R+N1vyTIafjmHLgD6AScrPE61NF5HCYBTwMzVPmh6CGXPUsCjCl721O2M/TToczdNpf7+tzHE2c/QVCANYsrSUmHsvhq8XamLN3Bsm3JiED3ptUZ1LE+A9rXpUaU9R8wxpiyVMZJwJFZgSVBXgP2arw+6v+8ROO1k6PzOEwCLgE+xDe5WA6+DgiqSrke6N6SAGPckeXJ4u5pdzN24VjObnY2E4ZMoFZkLbfDqpQ2JaXx9dIdTFm6g8Q9hwgMEPq0qMF57epyXts61IkJcztEY4yp9Mo4CVgBdNJ49UiCrAFGabz+cvg7jdf2js7jMAnYCFwMLFfNf2KD8siSAGPc9c7id7j1m1uJCY3htQGvWfOgUqSqrNmVypSlO5i2Yhebknz/93VqFEf/dnU5r10dWtSy+QeMMaY0lHES8DAwAEjCN6JmF41XlQRpCYzXeO3r6DwOk4DvgQtUj3YKrggsCTDGfct2L2Pk5JEs3LmQIScP4dUBr1I3qq7bYVVqqkrinkP8sGo336/cxbJtvrkdm9eK5Mw2tTmjTS16NKtOaJANXWyMMSWhrEcHkgTpBdQDftB4TfOXtQaiNF4XOTqHwyTgPaA58B2Qdbjchgg1xjjh8Xp4/vfnif85nsiQSMacP4arTrnKJhcrIzsOZvDjqt1MX72beZv2k+3xEh4cSJ8WNTijTS3OaFObRtUj3A7TGGMqLDeGCC0up0lAfH7lNkSoMaYw1iStYeTkkczZNocBrQYw9sKxNIptVPCBpsSkZ3uYu3EfP6/dy89r97JlfzoAjaqH06d5Tfq0rEHv5jWobX0JjDHGsUqbBFRUlgQYU/7kenN59Y9XeXDGgyjKnT3u5IFTH6BaeDW3Q6tyVJVNSWn8uj6J3zckMWfDPlIyPQC0rB1F7+Y16NGsOt2bVqdurCUFxhhzPJUuCRDhVVVuF+Fr+HuHYFUuKs3gisuSAGPKr80HN/PIzEf4cNmHxIbF8uCpD3JHjzsID7bx7t2S61VW70zh9w1JzE7cx/zN+0nPzgWgYbVwujetTrem1ejetDota0UREGDNuYwxBipnEpCiSowIp+f3vSqzSi2yEmBJgDHl37Ldy3hwxoN8u/5bGkQ3IOGMBEZ0GmFzC5QDObleVu9MYf7mAyzYvJ/5mw+QdMjXLSw6NIhTGsbSqVEcHRvF0blRnDUhMsZUWZUxCVisSucyjKdEWRJgTMUxa/Ms7p9+P/O2z6Nl9Zbc2eNOrut0HdGh0W6HZvxUlT/3pTN/836WbjvI0q3JrN6Zgsfr+ztSLzaM9g1iaV8/lnb1Y2jXIIa6MWHWAdwYU+lVxiRgGxx/BCAbHcgYU5JUla/WfMXTs59m3vZ5xITGMLLTSO7oeQfNqzV3OzyTj8ycXFbuSGHp1oMs2XqQFTuS2ZSUxuE/LdUjQ2hXP4a29WJoXSeaNnWjaVk7irBgG57UGFN5VMYkYCfwBr4Zgv/GRgcyxpSWedvm8fIfLzNp5SRyvbkMajOI27vfzlnNziIwwB4gy7O0LA+rd6awckcKK3cks2J7Col7DpGd65tqJkCgac1ITqobTavavqSgZe0omtWMtOTAGFMhVcYkYJEqXcownhJlSYAxFd+O1B28Mf8Nxi4cS1J6EvWi6jGs3TCuaH8FPRr0sKYmFYQn18vmfWms2ZXKul2prNmVytrdqWzZn36k1kAEGlWLoEWtSFrUiqJpzUia1YykSY0I6seGW0dkY0y5VRmTAOsTYIwpFzI9mXy99msmrJjAN+u/ITs3m+bVmjO83XAub385p9Q+xRKCCigzJ5eNe9PYsPcQiXsOHVlvSkojy3N0kvqQoACaVI+gSY1IGlUPp3H1CBpVi6BR9QgaVQ8nIsQ6khtj3FMZk4Dqquwvw3hKlCUBxlROyZnJfLnmSyasmMCMjTPI1VwaxjTkvObn0b9lf85udjY1Imq4HaYpBq9X2Z2ayaakNP7cl87mpLQj21sPpB8ZuvSwGpEhNKgWTv3YcOrHhVM/LowGcb7terFh1IgKJdBqEowxpaTSJQEVnSUBxlR+e9L2MHnNZH7Y+APTN07nYOZBBKF7g+6c1/w8Tm18Kj0a9LDJyCoRVWV/WjZbD2SwZX86W/ens+1AOtsPZrLjYAbbD2SQkfPXJCEwQKgdHUrd2DDqxoRRx7/Ujg6lVnQotWNCqRUVSrWIEGt2ZIwpNEsCyhlLAoypWjxeD/O3z+eHDT/w/Ybvmbd9Hl71NSlpU6MNvRr2olfDXvRs0JN2tdsREhjicsSmNKgqyRk5bPcnBLtTMtmVksmu5Cx2pWSwKzmT3SlZHMry/O3YoAChZlQoNaJCqBEVSs2oEN/nSN/n6pHBVI8MpXpECNUig4kKDbJmaMYYSwLKG0sCjKnaUrJSWLBjAXO3zT2y7E3fC0BQQBCta7Smfe32tK/Vnva129OudjuaV2tuE5VVEWlZHvamZrH3UBZ7UrLYm5rJntQskg5lkXQom33+ddKhrL/0T8grJDCAuIhgqkWEEBsRTFy4bzsuIpjYiGBiwoKJDfctMYfXYUFEhwUTEhRQxj+xMaa0WBJQzlgSYIzJS1XZfHAz87bPY/nu5azYu4IVe1aw8cDGI/sESiBN4prQoloLmldrfmTdJK4JDaIbUDuytg1RWsWoKmnZuew7lMX+tGwOpGez75BvvT8thwNp2RzMyOZgeo5vycjmQHoO2cdJHA4LCw4gOiyYaH9SEBMWRFSob4kMDSI6zLc+XBYREkik/7tI/3ZESCARIUGWUBjjMksCSpmInA+MAQKBcar61In2tyTAGOPEoexDrN67mhV7VpC4P5GNBzeyYf8GNh7YyL6MfX/ZN1ACqRddj/rR9WkQ3YB6UfWoFVmLWhG1jqxrRtSkZkRNqoVXIywozKWfyrgtMyeX5IwckjNySPGvDy+pmR5SMw+vPaRm+T4fyvRwKOvo4vRPdFCAEB4SeCQpCA/2bYeHBBIWHEj44SUkkNDgAMKCfOVhwQFH10G+70KDfJ9DgwIJDfKtQ4ICCA0KOLIOCrSkw5i8nCQBBT3Hiq9t4RhgAJAOXKeqi0op5IqTBIhIILAOOBfYBswHrlDVVcc7xpIAY0xxJWcms+HABrYmb2V76nZ2pO5ge+p2tqdsZ3vqdnYd2sX+jOMPohYWFEZcWNxfluiQaN8SGk1USNSR7YjgiCNLeFD4ke2woDDCgsIIDQo9sh0SGEKA2INYZaaqpGfncijLQ1qW58h2eraHtKxc0rI8pGXnkpHt+y49O5eM7FzSc3xlGTm+zxk5XjJzcknP9pCRnUumx1tgLUVBAsQ3bGtIYAAh/mQhOFB8ZUEBBAf6ltAj23KkLDhQCAr0HRsUcHjbtw4KFIIDfOugwACCA4TAAN+xQYHi2z8ggED/foEBQlCgb58g/75HtwMIFCHQf1yAHP3+8D6HywIE69thiqWgJMDJc6yIDADuwJcE9ATGqGrP0oq5IjV87QEkqupGABGZCAwGjpsEGGNMccWGxdKlXhe61Dv+vIker4d96fvYm76XvWl72Zu+l6T0JJIzkzmQeYCDmQePrJPSk9h0YBOHsg+Rmp3KoexDRzovF1ZwQDAhgSGEBIYQHJhnOyCY4MBgggKCCA7wr/2fDy+BEuhbBwQSKIF/WwdIwJHPARKQ7yLI0W2RI2Un2hb/BPSHt/OuAcfbh8+R3+e8ZceW53W8ffKW/+0YB+dyokQeOEMhOBSCgZhCxuRVxZOr5OR6yfH6kgJPrpLj9R4tz/WSk6t4vL59PV4vnlxvns+Hy3zbuf7tLK/i8SiebC+5Xj2yeHKVXD36+fC2x//ZbQIEBAgB/vWRz+LbFsG3Lb6kIUD8/y4P7+tPJCTvd/51gPguIAgBAXm+O2Y/39pfnue6vrUcOcfh6/GX8r+eI28Zea91+IcFAjgaV95/koevd7jsyPXz7CBHT+Pf76//4v7yOyUc893hGPP+3v11K++1817zb/sf+X/Ap1/TzlzW8TTKISfPsYOB99X3hn6uiMSJSD1V3VkaAVWkJKABsDXP5234siRjjHFVUEAQdaLqUCeqTqGPVVUyPBkcyj5Eek56vkumJ5MsTxaZnkzfdq5vOzs3O98lx5tDTm4OHq+HHK9/nZtDlieLdE3H4/Xg8XrI9eYe2faql1zNJdeb+7ftY5dczUVV8aoXRY+UG1NoAf6lPNNj1qZcW7nnZreSgCARWZDn81uq+laez06eY/PbpwFQ5ZOA/F5p/O1XUkRGAaMAQkJs+D9jTPkmIkea/VR0qoqiR9Ze9f4lWThcnt++ecsK2gaO+zlv2bHlx8aa3z4naiLr5FxOHO88JamiNPU15ZOqouqrMVLy5kF65IMq/t9hX5Fqnn/ZeQ7SPP/i8+73l3+i+pfVke+O/p7n/S6f3yA9um+DmJpF+6GLz6Oq3U7wvZPnWEfPuiWlIiUB24BGeT43BHYcu5M/63oLfH0CyiY0Y4wxR5r7WNNqY4w5lpPnWEfPuiWlvFfC5TUfaCUizUQkBBgOTHE5JmOMMcYYYwri5Dl2CnCt+PQCkkurPwBUoJoAVfWIyO3A9/iGVnpHVVe6HJYxxhhjjDEndLznWBEZ7f9+LPAtvpGBEvENEXp9acZUYYYILQobItQYY4wxxpS2ijhZWEVqDmSMMcYYY4wpAZYEGGOMMcYYU8VU6uZAIuIFMtyOowIIAjxuB1EJ2X0tPXZvS4/d29Jj97b02L0tPXZvnQlX1Qr1cr1SJwHGGRFZUMDYtqYI7L6WHru3pcfubemxe1t67N6WHru3lVeFyliMMcYYY4wxxWdJgDHGGGOMMVWMJQEG/DMsmxJn97X02L0tPXZvS4/d29Jj97b02L2tpKxPgDHGGGOMMVWM1QQYY4wxxhhTxVgSUIWIyGUislJEvCLS7ZjvHhSRRBFZKyL985R3FZHl/u9eFhEp+8grFhHpJCJzRWSJiCwQkR55vsv3PhvnROQO//1bKSLP5Cm3e1sCROSfIqIiUjNPmd3bYhCRZ0VkjYgsE5EvRSQuz3d2b4tJRM73379EEXnA7XgqMhFpJCIzRWS1///Yu/zl1UXkRxFZ719XcztWU3yWBFQtK4BLgV/yFopIW2A40A44H3hdRAL9X78BjAJa+ZfzyyzaiusZIEFVOwGP+D8XdJ+NAyJyJjAY6KCq7YDn/OV2b0uAiDQCzgW25Cmze1t8PwLtVbUDsA54EOzelgT//XoNuABoC1zhv6+maDzAP1T1ZKAXcJv/fj4AzFDVVsAM/2dTwVkSUIWo6mpVXZvPV4OBiaqapaqbgESgh4jUA2JUdY76Oo+8D1xcdhFXWArE+LdjgR3+7XzvswvxVWS3AE+pahaAqu7xl9u9LRkvAvfh+zd8mN3bYlLVH1T18GRLc4GG/m27t8XXA0hU1Y2qmg1MxHdfTRGo6k5VXeTfTgVWAw3w3dPx/t3GY88ClYIlAQZ8v+Bb83ze5i9r4N8+ttyc2N3AsyKyFd+b6gf95ce7z8a51kA/EZknIrNEpLu/3O5tMYnIRcB2VV16zFd2b0vWSOA7/7bd2+Kze1hKRKQp0BmYB9RR1Z3gSxSA2i6GZkpIkNsBmJIlItOBuvl89bCqTj7eYfmU6QnKq7wT3WfgbOAeVf1cRIYBbwPnYPfTkQLubRBQDV81dXdgkog0x+6tIwXc24eA8/I7LJ8yu7fHcPJ/r4g8jK+5xUeHD8tnf7u3hWP3sBSISBTwOXC3qqZYd8DKyZKASkZVzynCYduARnk+N8TXhGUbR6ut85ZXeSe6zyLyPnCX/+OnwDj/9vHus8mjgHt7C/CFv3naHyLiBWpi99aR491bETkFaAYs9f+xbwgs8ndqt3vrQEH/94rICGAgcLYeHZvb7m3x2T0sYSISjC8B+EhVv/AX7xaReqq6099UeM/xz2AqCmsOZACmAMNFJFREmuHrAPyHv8ovVUR6+UcFuhY4Xm2COWoHcLp/+yxgvX873/vsQnwV2Vf47iki0hoIAZKwe1ssqrpcVWuralNVbYrvwaqLqu7C7m2xicj5wP3ARaqanucru7fFNx9oJSLNRCQEX0frKS7HVGH5/9a/DaxW1RfyfDUFGOHfHoE9C1QKVhNQhYjIJcArQC3gGxFZoqr9VXWliEwCVuGrqr5NVXP9h90CvAeE42vH+t3fz2yOcRMwRkSCgEx8oytRwH02zrwDvCMiK4BsYIT/rard21Ji/25LxKtAKPCjv6ZlrqqOtntbfKrqEZHbge+BQOAdVV3pclgVWV/gGmC5iCzxlz0EPIWv+eUN+EYPu8yd8ExJshmDjTHGGGOMqWKsOZAxxhhjjDFVjCUBxhhjjDHGVDGWBBhjjDHGGFPFWBJgjDHGGGNMFWNJgDHGGGOMMVWMJQHGGOMSEflZRPofU3a3iLx+gmM2i0hNEYkTkVtLOb6mIrJNRAKOKV/in0gsb9mjIrJdRB4rxPkfFZEnjynrJCKr/dszReSQiHQrzs9hjDHm7ywJMMYY90zAN7lRXsP95QWJA0o1CVDVzcBWoN/hMhE5CYhW1fwmtXpRVR8pxCUmAJcfUzYc+Nh//TOBBYWJ2RhjjDOWBBhjjHs+AwaKSCj43rwD9YHfROQKEVkuIitE5Ol8jn0KaOF/K/+siESJyAwRWeQ/bvDhHUXk3yKyRkR+FJEJIvJPf3kLEZkmIgtF5Ff/A/6xjk1UHCUp/rf840XkB3/txaUi8ow/tmkiEqyqa4GDItIzz6HDgIkFnd8YY0zxWBJgjDEuUdV9wB/A+f6i4cAnQD3gaeAsoBPQXUQuPubwB4ANqtpJVf+Fb3bqS1S1C3Am8Lz4dAOGAJ2BS4G8TWveAu5Q1a7AP4H8miFNAi72z4ANvjf3Th/SWwAXAoOBD4GZqnoKkOEvhzxJhoj0Avap6nqH5zfGGFNElgQYY4y78r5pP/yWvTvws6ruVVUP8BFwWgHnEeC/IrIMmA40AOoApwKTVTVDVVOBrwFEJAroA3wqIkuAN/ElH3+hqruAlcDZItIJyFHVFQ5/tu9UNQdYDgQC0/zly4Gm/u2JwFB/vwOnTaGMMcYUU1DBuxhjjClFXwEviEgXIFxVF4lI4yKc5yqgFtBVVXNEZDMQhi85yE8AcFBVOzk49+FEZTeFe0jPAlBVr4jkqKr6y734//6o6lZ/rKfjq7HoXYjzG2OMKSKrCTDGGBep6iHgZ+Adjj5gzwNO948CFAhcAcw65tBUIDrP51hgjz8BOBNo4i//DRgkImH+t/8X+q+bAmwSkcsA/E2HOh4nzM+BARSuKVBhTABexNe8aVspnN8YY8wxLAkwxhj3TQA64n/AVtWdwIPATGApsEhVJ+c9wN+fYLa/4/Cz+JoMdRORBfhqBdb495sPTPGf5wt8o+0k+09zFXCDiCzF1+RnMPlQ1YPAXGC3qm4qoZ85r0+BdliHYGOMKTNytHbWGGNMZSQiUap6SEQigF+AUaq6qISv8ShwSFWfK+Hz/gz8U1VtqFBjjClB1ifAGGMqv7dEpC2+PgLjSzoB8DsEjBKRmELOFXBcIjITaA7klMT5jDHGHGU1AcYYY4wxxlQx1ifAGGOMMcaYKsaSAGOMMcYYY6oYSwKMMcYYY4ypYiwJMMYYY4wxpoqxJMAYY4wxxpgqxpIAY4wxxhhjqpj/BzfjsllrGerfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ih = Ih(nest.GetDefaults(\"ht_neuron\"))\n",
    "\n",
    "V = np.linspace(-110, 30, 100)\n",
    "plt.plot(V, ih.tau_m(V))\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Voltage V [mV]\")\n",
    "ax.set_ylabel(\"Time constant tau_m [ms]\", color=\"b\")\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(V, ih.m_inf(V), \"g\")\n",
    "ax2.set_ylabel(\"Steady-state m_h^inf\", color=\"g\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The time constant is extremely long, up to 1s, for relevant voltages where $I_h$ is perceptible. We thus need long test runs.\n",
    "- Curves are in good agreement with Fig 5 of Huguenard and McCormick, *J Neurophysiol* 68:1373, 1992, cited in [HT05]. I_h data there was from guinea pig slices at 35.5 C and needed no temperature adjustment.\n",
    "\n",
    "We now run a voltage clamp experiment starting from the equilibrium value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ih = Ih(nest.GetDefaults(\"ht_neuron\"))\n",
    "nr, cr = voltage_clamp(ih, [(500, -65.0), (500, -80.0), (500, -100.0), (500, -90.0), (500, -55.0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_h, label=\"NEST\")\n",
    "plt.plot(cr.times, cr.I_h, label=\"Control\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"I_h [mV]\")\n",
    "plt.title(\"I_h current\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_h - cr.I_h) / np.abs(cr.I_h))\n",
    "plt.title(\"Relative I_h error\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel. error (NEST-Control)/|Control|\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Agreement is very good\n",
    "- Note that currents have units of $mV$ due to choice of dimensionless conductances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_T Channel\n",
    "\n",
    "The corrected equations used for the $I_T$ channel in NEST are\n",
    "\\begin{align}\n",
    "I_T &= g_{\\text{peak}, T} m_T^2(V, t) h_T(V,t) (V-E_T) \\\\\n",
    "m_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(-\\frac{V+59\\text{mV}}{6.2\\text{mV}}\\right)}\\\\\n",
    "\\tau_{m,T}(V) &= 0.13\\text{ms} \n",
    "+ \\frac{0.22\\text{ms}}{\\exp\\left(-\\frac{V  + 132\\text{mV}}{16.7\\text{mV}}\\right) + \\exp\\left(\\frac{V +  16.8\\text{mV}}{18.2\\text{mV}}\\right)} \\\\ \n",
    "h_T^{\\infty}(V) &=  \\frac{1}{1+\\exp\\left(\\frac{V+83\\text{mV}}{4\\text{mV}}\\right)}\\\\\n",
    "\\tau_{h,T}(V) &= 8.2\\text{ms} +  \\frac{56.6\\text{ms} +  0.27\\text{ms} \\exp\\left(\\frac{V   + 115.2\\text{mV}}{5\\text{mV}}\\right)}{1 +   \\exp\\left(\\frac{V  + 86\\text{mV}}{3.2\\text{mV}}\\right)}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class IT(Channel):\n",
    "    nest_g = \"g_peak_T\"\n",
    "    nest_I = \"I_T\"\n",
    "\n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "\n",
    "    def tau_m(self, V):\n",
    "        return 0.13 + 0.22 / (np.exp(-(V + 132) / 16.7) + np.exp((V + 16.8) / 18.2))\n",
    "\n",
    "    def tau_h(self, V):\n",
    "        return 8.2 + (56.6 + 0.27 * np.exp((V + 115.2) / 5.0)) / (1 + np.exp((V + 86.0) / 3.2))\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1 / (1 + np.exp(-(V + 59.0) / 6.2))\n",
    "\n",
    "    def h_inf(self, V):\n",
    "        return 1 / (1 + np.exp((V + 83.0) / 4.0))\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.m = si.odeint(self.dm, m0, t, args=(V,))\n",
    "        self.h = si.odeint(self.dh, h0, t, args=(V,))\n",
    "        return -self.hp[\"g_peak_T\"] * self.m**2 * self.h * (V - self.hp[\"E_rev_T\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iT = IT(nest.GetDefaults(\"ht_neuron\"))\n",
    "\n",
    "V = np.linspace(-110, 30, 100)\n",
    "plt.plot(V, 10 * iT.tau_m(V), \"b-\", label=\"10 * tau_m\")\n",
    "plt.plot(V, iT.tau_h(V), \"b--\", label=\"tau_h\")\n",
    "ax1 = plt.gca()\n",
    "ax1.set_xlabel(\"Voltage V [mV]\")\n",
    "ax1.set_ylabel(\"Time constants [ms]\", color=\"b\")\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(V, iT.m_inf(V), \"g-\", label=\"m_inf\")\n",
    "ax2.plot(V, iT.h_inf(V), \"g--\", label=\"h_inf\")\n",
    "ax2.set_ylabel(\"Steady-state\", color=\"g\")\n",
    "ln1, lb1 = ax1.get_legend_handles_labels()\n",
    "ln2, lb2 = ax2.get_legend_handles_labels()\n",
    "plt.legend(ln1 + ln2, lb1 + lb2, loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Time constants here are much shorter than for I_h\n",
    "- Time constants are about five times shorter than in Fig 1 of Huguenard and McCormick, *J Neurophysiol* 68:1373, 1992, cited in [HT05], but that may be due to the fact that the original data was collected at 23-25C and parameters have been adjusted to 36C.\n",
    "- Steady-state activation and inactivation look much like in Huguenard and McCormick.\n",
    "- Note: Most detailed paper on data is Huguenard and Prince, *J Neurosci* 12:3804-3817, 1992. The parameters given for h_inf here are for VB cells, not nRT cells in that paper (Fig 5B), parameters for m_inf are similar to but not exactly those of Fig 4B for either VB or nRT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "iT = IT(nest.GetDefaults(\"ht_neuron\"))\n",
    "nr, cr = voltage_clamp(\n",
    "    iT, [(200, -65.0), (200, -80.0), (200, -100.0), (200, -90.0), (200, -70.0), (200, -55.0)], nest_dt=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EG5MjgsF2AFoDldABrY27vQ3IpNJoVT3Z6yCMyQS+zj7RlkWb9LIWd8bkiEhNdEdhFQDtzTZrYQ57TUSmeh2EMZlALIk2HrGRaGNyRMitiQ4VVQMQbLFe0TlsNnCBiKzDKecQQFX1MG/DMib9fG53Dpux0KSbjUQn2ZYtWzjnnHOYOHEihx56KKeeeirvv/9+n7dzww03dGmRl6jy8vI+v8bkhkhNtJYMAqCj2ZLoHHYKMAk4CTgN+Kz71Zi8s/fCQsuiTXpZEp1EqsoZZ5zBnDlzWLNmDStWrOBXv/oVW7du7fO2ekuiQ6HQQEM1OSjk1kT7Sp0kOtxqSXSuUtUPgWqcxPk0oNpdZkzesZFo4xVLopNo/vz5FBQUcMkll3Qumz59OrNnz+bKK69kypQpTJ06lYceegiAF154gTlz5nD22Wdz8MEH8+UvfxlV5cYbb2TTpk0cf/zxHH/88YAzwvzTn/6UWbNm8frrr3PdddcxZcoUpkyZwg033ODF2zUZJhxyRqL9ZU5bO7UkOmeJyLeB+4H93Nt9IvJNb6MyxhtWE228kps10U9eBVuWJnebw6fCKdf0usqyZcs44ogj9ln+yCOPsHjxYpYsWcL27ds58sgjOfbYYwF45513WL58OSNHjuSYY47h1Vdf5Vvf+hbXXXcd8+fPp6amBoCmpiamTJnCL37xCxYtWsRdd93Fm2++iaoya9YsjjvuOA4//PDkvmeTVSI10f7CUlq1ANob47zCZLGLgFmq2gQgIr8BXgf+6GlUxnigcyTahqJNmtlIdBq88sornHvuufj9foYNG8Zxxx3HggULADjqqKMYPXo0Pp+P6dOns379+pjb8Pv9nHXWWZ3bO+OMMygrK6O8vJwzzzyTl19+OV1vx2SocMhJon2BAM1Sgs+S6FwmQHRdV8hdZkzeaWh1jn2WQ5t0y82R6DgjxqkyefJkHn744X2W93axQ1FRUed9v9/fOetcd8XFxfj9/rjbM/kr7NZEi6+AZinF32FJdA67C3hTRB51H38OuMO7cIzxRn1zB8s3OaVrNu23STcbiU6iE044gba2Nm677bbOZQsWLGDQoEE89NBDhEIh6urqeOmllzjqqKN63VZFRQUNDQ0xnzv22GN57LHHaG5upqmpiUcffZRPfOITSX0vJvvsHYkuoNVXRsCS6JylqtcBFwI7gV3Ahap6g6dBGeOB3S3tnfdDNhRt0iw3R6I9IiI8+uijfOc73+Gaa66huLiY8ePHc8MNN9DY2Mi0adMQEX77298yfPhw3nvvvR63dfHFF3PKKacwYsQI5s+f3+W5GTNmcMEFF3Qm4l/72tesHtp0Xljo8xfQ5i+lINTkcUQm2UTkSKBGVZ9U1beBt93lp4uIT1UXeRuhMekVfWLWTtKadJNsLA2YOXOmLly4sMuylStXcsghh3gUUeaz70/uW/riI0ydfyHvnfowrS/8nvK2bRzwk7e9Dst0IyKLVHVmP1/7AnCBqq7vtvwA4FZVPWHgESZfrGO2Mcmwtq6RE37/IgB3XjCTEw4e5nFEJhf1dNy2cg5jckQ45PaJDhQQDJRRHLaR6Bw0pHsCDaCqq4Eh6Q/HGG9FV3CEwt7FYfKTJdHG5Iiwe1Gqz19AqKCcMu37jJcm45X08lzZQDcuIneKyDYRWdbD8yIiN4rIahF5V0RmDHSfxgxE9Nl06xNt0s2SaGNyhIbdPtGBAOHCSkuic9NzIvJLicwu4RKRnwP/TsL27wZO7uX5yHTjk4CLgT8nYZ/G9Ft02pyN5akmu+VUEm1/QLHZ9yU/aGjvSLQUVVAoQdpaLZHOMVcA+wOrReTv7m01cBDwXwPduKq+hNPxoydzgXvV8QZQLSIjBrpfY5LBmnOYdMuZ7hzFxcXs2LGDIUOG0G2QJq+pKjt27KC4uNjrUEyKaaQ7R6AAKakEoLF+J0XFpV6GZZLInaHwXBHZH5jsLl6uqmvTFMIoYGPU41p32ebuK4rIxTij1YwdOzYtwZn8ZuUcJt1yJokePXo0tbW11NXVeR1KxikuLmb06NFeh2FSLNIn2h8oxF9SBUBzwy6GDLOffa5xk+Z0Jc7RYo1QxMxcVPVW4FZwunOkMiiTv3xRv5E2Em3SLWeS6IKCAiZMmOB1GMZ4J6omOuAm0a2Nuz0MyCSbiKyjh6Q1sor7/A2qemMKQqgFxkQ9Hg1sSsF+jEnQ3izaShdNuuVMEm1MvovURPt9AQrKqgFoa9zlYUQm2VTV65GCx4HLRORBYBZQr6r7lHIYky7SZSTakmiTXpZEG5MrNASALxCguLwagPameg8DMskmIv8LPAU8p6oNKdj+A8AcoEZEaoGfAQUAqnoLMA84FVgNNONMPW6MZ6Lri8LWJ9qkWcqTaBE5GfgD4AduV9VrYqwzB7gB52C9XVWPS3VcxuQadT9BfD4/JRWDAQg27/YwIpMCd+G0oPsvEWkHngGeUtUlydi4qp4b53kFLk3GvoxJhuixZxuJNumW0iRaRPzAzcCncGrpFojI46q6ImqdauBPwMmqukFE9ktlTMbkLHWSaPH5Ka2oBiDcusfDgEyyuW3l3gCuFpEhwEnAFSJyGPA2TkL9f17GaIxXLIc26ZbqkeijgNWR9ktuHd1cYEXUOl8CHlHVDQCqui3FMRmTmzqTaB+l5U6LO7UkOmep6g7gAfeGiBxB7xOlGJPTQpZFmzRLdRIdq6forG7rHAgUiMgLQAXwB1W9N8VxGZN7NFLO4aOgsIhmLUJarSY6F4nITOATwEigBViGUyf9S08DM8ZDVs5h0i3VMxYm0lM0ABwBfAb4NPATETlwnw2JXCwiC0VkofWCNiaGqCQaoElK8bUn/doz4yERuUBE3gZ+AJQAq4BtwGzgWRG5R0RsZhOTlyyHNumW6pHoRHqK1uJcTNgENInIS8A04P3olaxxvzG907DbncPnB6DFV4q/o9HLkEzylQHHqGpLrCdFZDowCdiQzqCMyQQ2Em3SLdUj0QuASSIyQUQKgXNw+oxG+wfwCREJiEgpTrnHyhTHZUzOEfcDxOd3/jdu9ZVRELQkOpeo6s09JdDu84tV9fl0xmRMpgjblIUmzVI6Eq2qQRG5DHgap8Xdnaq6XEQucZ+/RVVXishTwLtAGKcN3rJUxmVMLtJIn2i3nKPNX06RJdE5RUR6nYVQVb+VrliMyQTRg8+WQpt0S3mfaFWdh9OgP3rZLd0e/w74XapjMSanRUai3XKOjkA5lR12/UCOWeR1AMZkqpCNRJs0sxkLjckVkRZ37jy4wYJySpqavIzIJJmq3hP9WEQqnMVqpxxM3rOSaJNuqa6JNsakiWiYsArilnOEiyop02aPozKpICJTROQdnNZ2K0RkkYhM9jouY7xkFxaadLMk2pgcoRomHNVVUosqKZNWQsGgh1GZFLkV+C9VHaeqY4ErgNs8jskYT1k1h0k3S6KNyRXdkmgpqgCgsWG3RwGZFCpT1fmRB6r6Ak77O2Pylo1Em3SzJNqYXKGKRv1J+0uqAGjes8OriEzqrBWRn4jIePf2Y2Cd10EZk357E2e1JNqkmSXRxuQI0VCXkWh/qZNEt9hIdC76KjAUeMS91QAXehqRMR6zcg6Tbtadw5hcoWE0KokuKB0EQGvjLq8iMikgIn7gb6p6otexGJNJrJzDpJuNRBuTM5Rw1J90UbkzEt3etNujeBLT3tbKGw/8ipamBq9DyQrqzKrTLCJVXsdictfGnc38+LGlWdV72WYsNOlmSbQxuULDhGXvSHRpZQ0AwabMHole8uQdfGzVb1jy6O+9DiWbtAJLReQOEbkxcvM6KJM7vvnAO9z3xgaW1O72OpSE3fjv1V6HYPKMlXMYkyMkHOpSzlExaCgAwcbMvrAwuGsDANq80+NIssq/3Fu0pAzDicjJwB8AP3C7ql7T7fk5wD/YeyHjI6r6i2Ts22QOG9M1Jj5Loo3JGUoYf+ej8qohhFXQlsweiRZ/ofM1bP2s+6BaVf8QvUBEvj3Qjbr11jcDnwJqgQUi8riqrui26suq+tmB7s9krsi/41ZmbEzPek2iEzw9uEdVf5ykeIwx/dWtT7Q/EKBeSvFleBKNW4IiwVaPA8kq5+OMFke7IMayvjoKWK2qawFE5EFgLtA9iTY5bm9lWGZn0ZbkGy/FG4meC/w0zjpXAZZEG+O1bt05ABqkAn97vUcBJUaDbQBIqM3jSDKfiJwLfAmYICKPRz1VASSjbmcUsDHqcS0wK8Z6R4vIEmAT8F1VXR4j1ouBiwHGjh2bhNBMOmXjSPTkkZVeh2DyTLwk+npVvae3FURkUBLjMcb0k2i4S3cOgGZ/JYUZnkRLqAMAX8hGohPwGrAZpy909JWYDcC7Sdi+xFjWPY16Gxinqo0icirwGDBpnxep3oozPTkzZ87MolTMZKviAn/8lYxJonhJ9CvxNqCqNyQnFGPMgMQYiW4NVFIczOwkWt0RaH+wxeNIMp+qfgh8CBydol3UAmOiHo/GGW2OjmFP1P15IvInEalR1e0pismYhLR2hLwOweSZeC3ubhORD0TkFyJyaFoiMsb0i9B12m+A9oIqSkOZ3X85MhIdCFkSnSgROdM9NteLyB4RaRCRPfFfGdcCYJKITBCRQuAcILpsBBEZLuJUzIrIUTifI5ndAsb0mfsjzvCK6K7agmGvQzB5pteRaFU9XEQOwjmQPiwi7cADwIPuiIgxJlN06xMNECqqorwh05NoZyQ6ELZyjj74LXCaqq5M5kZVNSgilwFP47S4u1NVl4vIJe7ztwBnA/8pIkGgBThHNZsqZ00iIkeSlvbsGd1ttyTapFncyVZUdZWq/lxVD8W5Irwa+LeIvJrq4IwxiZMY5RxaPIhKbSIcytwPQgk7I9EFYbuwsA+2JjuBjlDVeap6oKpOVNVfustucRNoVPUmVZ2sqtNU9WOq+loq4jDe2tnUDsB3/7bE40gS1xbM3OOcyU0J94kWER+wHzAMKAPqUhWUMaY/9i3noHQwPlHqd2+nasgwb8KKwxdyPqwL1Uai+2ChiDyEc1Ff538fqvqIZxGZnBEOK2u3NwGwrSGz/7mNPgXS2mEj0Sa94ibRIvIJ4Fzgc8Ay4EHgclXN7KuVjMkzsUai/WWDAWjYVZexSXRkJLrQRqL7ohJoBk6KWqaAJdFmwDrC2ZmM2ki0Sbd4k61sBDbgJM4/V9WtaYnKGNN3GiYsXUeiCyucJLp5T+Y2ThCcD+wiLIlOlKpe6HUMJndJzE6Hma8tGEZVOy+KNCbV4o1Ez7YLCI3JDsK+I9FFFTUAtNZnbhIdmc2hWC2JTpSIjAb+CByDMwL9CvBtVa31NDCTE7I1B1WFjpBSGMjSN2CyTq8XFkYSaBGZKSKPisjbIvKuiCwVkWQ09jfGJIlTztH1T7q0ykmi2xsztwOZuFWNJdKe0RdAZpi7cFrPjcSZZfCf7jJj8lqrlXSYNEr0wsL7gSuBpUB2FksZk+tilHOUVw8FINS004uIEqN7DymtLY2Ulld5GEzWGKqq0Unz3SLyHa+CMbklmxsWtnWEodjrKEy+SDSJrlPVx+OvZozxSqzJVioHOUl0uCnzR6IBWpstiU7QdhH5Ck7ffnAu/s7cH7IxKdI94W9uDwJFnsRi8k/cPtGun4nI7SJyrjtT1pkicmZKIzPG9Ems7hyBgkJ2U46vJXPzK4kaiW5rafQwkqzyVeALwBZgM84EKF/1NCKT1cJh5erHl7NhR7PXoQzIbS+v9ToEk0cSHYm+EDgYKGBvOYe1UzImk2gYlX3/L673DaKgJZMvLNybRLe3NHkYSPZQ1Q3A6V7HYXLHsk313P3aet7ZsIv/u+Ror8Ppt7GDS70OweSRRJPoaao6NaWRGGMGxCnn2Peq9MZANcXtmVsTHV3O0d5qSXRvROS3wNrI7IFRyy8Hhqvq972JzGS7SFmEAs+v3OZpLANRVpTwHHLGDFii5RxviMih/dmBiJwsIqtEZLWIXNXLekeKSEhEzu7PfozJd7G6cwC0FQ6mPLTLg4gSE13O0WFJdDyfBW6NsfwPwGfSHIvJIdGlxbua2z2LY6AaWoNeh2DySKJJ9GxgsZsMJ9ziTkT8wM3AKcChwLmxknF3vd8ATyceujEmmhBGYzR47SiuoTq8O/0BJUgIE1In7qDVRMejqrpPhyR3mTXHNQOWzb9EPoGG1g6vwzB5JNEk+mRgEs4Us6fhjIaclsDrjgJWq+paVW3Hmflwboz1vgn8Hcjec0jGeEx03+4cAOGyoVTSRHtbqwdRxSeqNLs9qULtNhIdR7OITOq+0F3W4kE8JkdoNve1cw0uK2RHY3aMoneEwpx76xu8tS5zS+1MfAkl0ar6YaxbAi8dBWyMelzrLuskIqOAM4AuNX7dicjFIrJQRBbW1dUlErYxeSb2SLSv3Glzt3v75nQHlKAwLVICQKgtuzsDpMFPgSdF5AIRmereLgT+5T5nTF7RqEKUEVUlbK7PzMGC7rbUt/L62h1c/tBir0Ppt407m3Pin6+B6DWJFpG3420gzjqxzgx1/47fAHxfVXudZkhVb1XVmao6c+jQofHCMibviCqx/qQLq4YDsGf7R2mOKDGiSpu4I9GWRPdKVZ8EPgccD9zt3uYAZ6nqPK/iMiYTjKgqZnN9dpyQKfA7x+qOUHbOX7dy8x4+8dv5/OWNRMZTU+f2l9eyYL13o/nxLmM9JE7tswC9zYxQC4yJejwa2NRtnZnAg+KMoNUAp4pIUFUfixObMSaKECYs+/5Jl1QPA6B555Z0h5QQIUybrwRCoFbOEZeqLgPOjzwWkeGqmpk/3Dy0aXcLH7/m31xz5lSmjKpiyqjsmjxoZ3M7P3p0WZdlu5vbue3ltXz9E/tTXVroUWTxjagq5qUPsuNMdcDvXgcSzs6R3NXbnOtX3li7g/OOHt+n17YHwxQGEqsmnrd0M8s+que8o8czvGrfqSj/518rAVh/jTfXVcdLog9OYBu9jSAvACaJyATgI+Ac4EvRK6jqhMh9EbkbeMISaGP6rqea6IqakQC01W9Nd0gJEVXa/KWWRPffPGCG10EYx7u1uwG46pGlALzzk08xqCxzE8+ISCq3cee+I7n3vv4hN89fw83z17D855/O2DZyW/a00toR5t/vbeWEg4d5HU6vIqfpdzZlRw13d5HfF+njpairtzVy4nUvcucFMxP6Gf3+mVWsqWviTy+sAeCfl81m6uh9/zENh5VtDW0xE+1U6vVfgZ5qobvdant5fRC4DKfrxkrg/1R1uYhcIiKXJPetGJPffIRiTrZS5SbRoYYMTaIJE/IV0a4BaLdyjn5IakOFeG1JxXGj+/y7ImIJfJT2UNeRxcP/+1laO3qtVsx40SUS85Zm6rUVMG1MNQDvbNjNOxt28d9PrMj7mt1U+NvCjSx0SyiKChLtTwGhsPL0cuek2T+XxP89am4Psn5HM5XFe/9pe3Zl7M+x8+58i4/9+nn++PwHaf2ZJ/7u+0lV56nqgao6UVV/6S67pftkAe7yC1T14VTHZEwuEjRmEl1WXkWrFkBTZs5aGJkkplmK8XVYi7veiMgzMRbflsTtJ9KW9BScbk2TgIuBPydr/7lgcFS5w7ghzux5x/1uPqGo0/a7mtppD4bZuqeVxjanr3E4rLQHvauP7S3v2LS7lQOHlTOquoQ//ns1dQ1t6QusD77+if0BaG4Pccl9i7jjlXVs2ZOZFxpGf7ub2/vW2zoYcn53ln1Uz9La+rT/o3Dlw+9y7+tOLXRRwN/rupvrW1i9rZGTrn+RiT+cx++eXgX0XsbyXw8t5qTrX+T1NTsIhZUfnHpI53M7GmP/7r2yejs15YX8/tn3+fWT7/X1LfVbZp6TMcb0WU+TrYjPxy6pxt+SmbWComEQH60U4wtmx0VBHtrnqmpV/VMSt9/ZlhRARCJtSVdErTMXuFedT+43RKRaREaoalKHKJ9fuZV3NuzGicNZ1mXI3V0oXR92Ob3c/XWdj2N0sen++lj77GmdrnE7nVpv/tIMTp4ynON+N5/aXS184/5FnH/0eG6av5rX1uzo8pqxg0vZ1tBKWzDM/jVlTNqvguFVxQwpK6S4wI/PJwR8gs8n+CXqvg/8Ph9+2XvfFxWT6t5kTdXpYxEr3xKBtzf0PCHT5voWxg8p45I5E/nybW9yzG/+TUVRgOICPyWFfkoKnFtxoZ+yQj+DygqpKA5QUuCnKODv+efQw/cx+uez72u6rrNqa0PnupGL9e54ZV3nsm/c/zYFPh/HHFDD+R8fR0dIaQ+F2dHYxvbGNnY0thOOk4T2VLIQVmVHUzvb9rSy3S3L8IngF+erzyf4ut33iyAi7I6a0OaMm1/jwYs/RktHiN3NHWxtaGVdXRN7Wjuob+mgvrmDusY26hqc287m9i4/x6mjqpg2poqw0rmPyO+K39f1fuTmc39nfO7yUFjZ3dzB7pZ2WtrDBMPhfb7f4r6XaEVxaps/e+Mr7IhRsrJh575nHddtb+LHjy3l1dXO38dF9ywE4OTJw/mBWx7VvftKeVGAqpICfnXmVI6dVMPPHl/OrS+tJeATLpkzkUK/j4bWILub29nV3EFVSQEHDa/oNea+6DWJFpFnVPWkpO3NGJMyPY1EA9QHaihpzcw27JG423wl+INWEx1HlYic2dOTqvrIALcfqy3prATWGQV0SaJF5GKckWrGjh3b50Be/mA7f3njw85Rtug0JxvO0I8aVILfJ7z8veO59aW1/PrJ93h6+VYqiwNMH1PN4o27ARhZVczkkZWcULkflSUFrNy8hw+2NfDqmu0ZMfve2MGlfLSrhY9PrGHG2EHc//VZPLl0My0dIVraw7R2hGhuD9LSEaK+pYOPdjWzu7mDxrYgbR6MrB+9/xBeX7v3n5R3NuxmSFkhCz7cyfXPvZ+SfVYWB6gpL0IEwuok12FVwmHnfiisXZaHwtrlZ7tqawOH//ezMbddURSgsqSAoRVFjBlcyoxxgxhaXsTQiiJqyouo3dXME+9u5l/vbsbvE8LqlE2Ew0qoc997Y+iNT6CqpIDSwkDnhY8Q+WfMeT/dR717K+dQ958MgFOmDOf6L06nKODjx48t44l3u/7PvXVPK6f98RUa24IcMW4QxxxQw5/mr+bQkZUMKivk2AOH8tL7dfz7vW2s297EhJoyWjtCtAfDfOawERx3oDO+8LPTJtPYFuRPL6zprKOOdtaM0fz+C9N6/0b0QbyRaOslZ0yWkF4mrWsuHkpN874HlEwghFGENl8JARuJjqcKZ7KrntqHDjSJTqQtaSLroKq34k5RPnPmzD6nvVefPpmrT5+c0LqdiXbUXrT7c52PI8/vXbl7Ut7bOj1tN7LOcyu2csXfljBhSBngjOD9v+MmsnVPG29v2MVvzz6MA4c5I2HN7UFKC3v+GG4LOklCOAwhVYLhvfdDob1JUvRNu/0oBOky0ivSdWQ1sv7P/7ki5sQfkRHDaWOci7lmjB3EjLGDeow5WjisnYl0ZD/xvo+dP4tuP4OeXvfXNzfw+2f3Jsf3fPUoDvzxk13ieP6K41i/o5kH39rAAfuVU14UYEh5ETXlhQwuK+wcwY4l3i/u4NJCSgp7L2mI5d3a3Zx+06sAXP/Faby2egczxg1iUGkBVSWFHDS8gsriAIFeYov4mlvGEo+qk0h3+T1yE26fT6goCuDzxf4MiXbsb+d3/l6EQj1/h7bucUovfvLZQ7lodmcPCSbUlFHf0sGupnYGlRXS0NrB/W9uoLEtyF+/PouPT6wBYO70kQxyy6Nu+coMzrj5NVZtbeDsP7/Gk9/5BC+sqqM9FGbOgXtTVb9PuO4L0/nSUWN5a/1OBKG8yE91aSHVpQWMGVSa0PcqUfGS6FSPehhjkqS3kej20hHUNLyJhsOIL+WXQvSJTxXER4evhEDIkug4PlTVr6Zw+4m0JU1knbSKnNqPVV6R7omszzpiNGcdMXqf5T89rXtpOb0m0ODUm8arOU2WqaOqep0972P7D+nzNn0+6VeC2RdVpQVdHhcGfHxiUg0vf+BcA3Lk+EFUlxYyvbSQ6e6Fh5kg+h+ZMw4fzRmH7/s7k/R9uqUmft/AfibRpRi91Ta/uc45I3Dk+K7/dB2wXzkAK7fsYeueVi5/aAkAsw+o6UygASYOLe+8X1oY4OnLj2XVlgZO++Mr/PJfK1m/o5kDh5Vz9MR9fzdnjh/MzPGD+/Hu+iZuEk1qRz2MMUkihHvKIqByBKXb2tizZxeV1X3/MEwlZyTaR4e/hPL2zKzbziCpzgjjtiUFHgcuc+ulZwH1ya6HNukX6iUZGllVzIiqkjRGMzB/uWgWqsqrq3dw5ITERs3TradDdTY46dBhPLPC6ZLRHmOymO2Nbfzl9Q/5w/MfEPAJk0d2bUkXqUn+nydWsmLzns7l3z5xUtx9HzS8gkvmTOTG5z8AnFHuWNc4pEu8JDrVox7GmCRxulzEHmUODHJGOXZuXpeBSbSCQChQSlGrjUTH8R/RD0RkCHAssEFVFw1046oaFJFIW1I/cGekLan7/C04falPBVYDzcCFA92v8V7kQrJYxg5J7inwdBARZk+qib+iR7Khd3hPbjz3cBau38VX7niTv765gW+dMKlLf+Yv3PI6a7c717d858RJ+LuViOxXUYwInQn0H86ZTl1DG0ckWCb0jTkT+de7m9i6p40zDh+VpHfVP/GS6Cz+X8mY/OJzu1zEUjrEOfvesG0DHDIznWHFFRmJDgVKKdLMbEeVQa4RkatUdZmIjADeBhYCE0XkVlW9YaA7cKcPn9dt2S1R9xW4dKD7MZmlt4s1h5QXpS+QPDGqOntG9rsrLvB3+Qdl0Ye7+MxhIzofRxLoqaOquOyEfUeX/T7p/H37xpyJzJ3et0S4uMDP3//z47R0hBjs8T8j8ZLo/4jzPAAi8rqqHp2EeIwx/dRbTXTVfuMAaN2xMebzXhK3JjpcUEqJ2kh0HBPcab/BGQF+VlXPE5EK4FXgBs8iM1mtt84NQzM4ic6GTi09+ft/Hk1xQXpq3lNp5eY9nUl09MRC35gzMe5rEynhiKW6tJDqfr0yueLNWList+ejpHeeRWPMPoSeu3MMGeG0GAvWe3r9V0yR5F8LyiilFe3ltLKhI+r+J3FHjFW1AbBvnOm3OQf13Iyrpjx7Sw8y2RHjBu9TL5yNbpq/mmfcmQgj05hfc+ZUTpk6osfXPHTxx7j289PSduFsqiTrMv0s/l/QmNzQ20h0cUkZu6jE15CJSbSb/BeW4Relrc1Go3uxUUS+KSJnADOApwBEpAQo6PWVxvSit+4bVs5hejOiqphrnnJmCdzT6vyfX1Hc++Fo1v5DODtGF5tsk1m9rowx/dZbTTTATn8NxS1b0xhRYiLJv6/IaWfU0rgnzivy2kXAZOAC4Iuquttd/jHgLo9iMjmgtwYHNZZEm158/ojRrK1roi0Y6pxEprIkPybETlYSbRcgGuMxnztpSU8aCvejsi3zkuhIn2gpcianaGmyJLonqrpNVS9R1bmq+kzU8vlYPbRJkh9/5pAuj4dYOYeJodrt0X2AO3nQC6vqaHBHosuL8iOJTta7TOgCRGNM6ggKvTTRby0fw6TmxRk34UpkxkJ/kXMgbm9p8DiizCUir6jqbPf+X1Q1+tj7Fk6JhzF9Fv3vt6/bsHQmX1hovPPilccTCiul7oQ6b63byeFjqwEoy5MkutdPUhFpEJE9MW4NItI5XNSHCxCNMSnSW000AIPGUyat7NqeWfNiOH2ifQRKnHKONhuJ7k1Z1P3uc2LbGUGTFNF9fccPKc3odmzZPGlJtqsqKWBwWSHFBX4OHl7BHa+so7nd6c5RnOUXDCYqXneOClWtjHGrUNXKdAVpjInP10t3DoDiofsDsL32gzRFlBgfYVR8FJQ4I9EdNhLdm94u4rYLvE2/Rc/65otKol+48vgujzNNNre4yyUnTxkOwL9XbgOguDBzznamUn68S2PyQGREtydVo5x+nHs2Z1YSHYm70JLoRFSLyBkicpZ7/0z3dhaQ/b2yTEbw2/Cu6aNzjnTaqD7ltrrLhf7XiciPohVj8oCP3rtzDBt7IADBunXpCikhPjeJLqkYDECwud7jiDLai8DpUfdPi3rupfSHY3KR34bXTB9FT/sNUGJJtDEmm8SriS4tr2I71fjq16cvqARE+kSXVzlJdKhlt6fxZDJVvdDrGExu6n5h4cHDK3hvi50VMom76UuHc9lf3wGgIE/+E7Mk2pgcEWkV15vtgRGUNdWmKaLE+Nzkv6xyEADaYiPRPRGR83p5WlX1L2kLxuQsv0949BvH0Nwe9DoUk0U+e9jIziQ6X1gSbUyOiHdhIUBj6ShG71mSnoASJO4kMYGCQpq0GGmzJLoXR8ZYJjhlHaMAS6JNv0SXQft9Qkmhn5LC/Dglb5Jn4tAy1tQ1eR1G2lgSbUyOiHdhIUBH9f7sV/88rc2NFJeWpymy3vmi4m6UMnxt1uKuJ6r6zch9cdopfBn4PvAG8Euv4jLZL7rLhWTRhYVq7Tkyyj8um00onD8/k/woWjEmDzhlEb2PHBUOPxSfKB+tfjdNUcUXnfw3+8op6LAkujciEhCRrwErgBOBs1X1i6qaOT9Uk3XCUcmodecw/VVeFKCqpMDrMNLGkmhjcoQQjjvzwODxUwDYtSFz5kdy+kQ7cbf6yynssIuZeiIil+Ikz0cAJ6vqBaq6yuOwTA6IHj3Mk2vCjBkwK+cwJkf4EijnGLn/ZILqo2PLyjRFFV903G2BCsrb6zyOKKP9EdgGzAb+GXXaXXAuLDzMq8BMdiuPmqa5+7TfxpjYLIk2Jkf4UCROEl1UXMoG/wiKdmXOhCvR5RzBggpKWjOrj3WGmZCqDYvIYOAhYDywHviCqu6Ksd56oAEIAUFVnZmqmEz6BPw+DtivnNXbGrtM+53psql+2+QeS6KNyQEaDuOT3vtER+womcCQlsxJVKMniQkVVlKm+XNldz9s0DhXUomIxFunB1cBz6vqNSJylfv4+z2se7yqbu/HPkwGC7jJcyZP821MJrHKJ2NyQGfOlEAS3Vo9iVGhTbS3taY4qsT4Ze9IdLiokgptQsNhj6PKWPNF5JsiMjZ6oYgUisgJInIPcH4/tz0XuMe9fw/wuf6HabJRZATaLiw0JjEpT6JF5GQRWSUiq93Rje7Pf1lE3nVvr4nItFTHZEyuCYdDzp0EPvwKRk4mIGE2rno7xVHFtzdZduKWkir8ojQ1Wq/oHpyMU0bxgIhsEpEVIrIW+AA4F7heVe/u57aHqepmAPfrfj2sp8AzIrJIRC7uaWMicrGILBSRhXV1VueeDQLuFYXZVM5hLe6Ml1JaziEifuBm4FNALbBARB5X1RVRq60DjlPVXSJyCnArMCuVcRmTa8KRZDSBJHr4QUfDAtjxwVtMPOzjKY6sd+FwGD90jkT7SqoBaNy9nXJ3BkOzl6q2An8C/iQiBUAN0KKquxN5vYg8BwyP8dSP+hDGMaq6SUT2A54VkfdU9aUYsd6Kczxn5syZlulkgc5yDhuJNiYhqa6JPgpYraprAUTkQZxThp1JtKq+FrX+G8DoFMdkTM5RjSTR8U8ujdr/UPZQim7yfnrWvXE7H9oFFUMBaNi5heFjJ3kVVlZQ1Q5gcx9fc2JPz4nIVhEZoaqbRWQETheQWNvY5H7dJiKP4hzn90miTfaJjEAr9j+PMYlIdTnHKGBj1ONad1lPLgKeTGlExuSgzrKIOJOtAIjPx4aiAxlUvyLuuqkWGUEXN+6SQc4gafOuLZ7FlMceZ2899fnAP7qvICJlIlIRuQ+cBGRO03EzIAV+J4kOhiyJNiYRqU6iY50TivnXKSLH4yTRMa8Gt/o6Y3oWqYlOtN1T46DJjO9Y6/nFhZG4I5OtlLtJdHt9zEFQk1rXAJ8SkQ9wSvCuARCRkSIyz11nGPCKiCwB3gL+papPeRKtSbqAz201aRf2GpOQVCfRtcCYqMejgU3dVxKRw4DbgbmquiPWhlT1VlWdqaozhw4dmpJgjclWe2uiE/uTLhgzg0IJ8uHKBSmMKj7tHIl24q4aOhKAUKMl0T0REb9b25xUqrpDVT+pqpPcrzvd5ZtU9VT3/lpVnebeJqvqL5Mdh/FOgXthYYeNRBuTkFQn0QuASSIyQUQKgXNwThl2cls1PQL8h6q+n+J4jMlJfU2iRx02B4AdK15MUUSJ6ewq4o6AlVdU06YF0Ghnm3qiqiGgWUSqvI7F5BYr5zCmb1J6YaGqBkXkMuBpwA/cqarLReQS9/lbgJ8CQ3CuNgebAcuYPtM+JtHDxxzARzKMotrX4q+cQt2Tf/H52CVV+FtinpAye7UCS0XkWaBzdhpV/ZZ3IZlsN7isEICA37pzGJOIlM9YqKrzgHndlt0Sdf9rwNdSHYcxuUw7+0QnfnLpo6ojOHD3S4RDIXz++BckpkKsEfRGfxWF7Ts9iSeL/Mu9GZM0Pzz1EMYPKeNThwzzOhRjsoJN+21MDui8sNCXeBIt42dTvXgea1cuZP8pHrVmj5H8NxcMprRjlzfxZAlVvcctkTvQXbTKbXlnTL+VFQX4+rH7ex2GMVnDpv02Jgf0tSYaYMyMkwDYtsS75grhbhcWArQVDaYiaEl0b0RkDs4shTfjTL7yvogc62VMxhiTbyyJNiYX9COJHj52Eut846n48NkUBRVfOMZIdLBsGEN0F6Fg0KOossLvgZNU9ThVPRb4NHC9xzEZY0xesSTamBwQjsz8F7M1e8+2jDieg9uWsXu7N5ObxJpp0Vc9hgIJsWPrxh5eZYACVV0VeeB2NirwMB5jjMk7lkQbkwMiyWhfaqIBhhxxBn5RPnjl76kIK65YXUWKhowFYOfmtV6ElC0WicgdIjLHvd0GLPI6KGPSzZrxGS9ZEm1MDug+aUmiDpg2m20MpnDVYymIKr5YF0RWDpsAQNO29V6ElC0uAZYD3wK+DaxwlxljjEkT685hTA6IXKCnfUyifX4/a0aexlEf3UvdpvUMHTk+BdH1LFbyP2TURAA6dlo5RyzifLMWqeoU4Dqv4zHGmHxlI9HG5ID+jkQDjD7+IvyirH7ujmSHFVes5L+yajBNWgz1tWmPJxuoU7uzxJ3t1RhjjEdsJNqYHKD96BMdMWbSNFYWTGbcugcJdvyEQEFhssPrUazkX3w+6vxDKWr6KG1xZKERwHIReYuuMxae7l1IxhiTXyyJNiYHqPZ9xsJorUd+g0Neu5SFT97JzNPTV1rbU/K/q3gsg1s3pC2OLPRzrwMwxph8Z+UcxuSAcNi9Rr0fI9EA0z55Lut9Y6hZfHNa+zP3lPy3Vk9kRGgTHe1taYslW7g10Ter6ovdb17HZky69a2ppzHJZUm0MbkgMqLbz5Fon9/PjpmXMz68gYWP3pDEwHrXU/If2O8gCiXElg/fS1ss2cJqoo3Zy1rcGS9ZEm1MDohMttLfJBpgxskXsrxwKgctv56d29JUjxxJ/ruNJ1WOPhSAHeuXpyeO7BOpiX5eRB6P3LwOyhhj8onVRBuTA/ZOWtL/k5vi81H6uespeegU3rvzfKqvfBqf35+kCGPrTP59Xfcz4oBpALRsWpbS/Wcxq4k2xhiP2Ui0MTkgcoEeMrCkd8KhR7L40CuZ1rqAN++8IgmR9a6zO0e3co7K6iHUynCKty1JeQzZyK1/Xo8z/feLwALg7YFsU0Q+LyLLRSQsIjN7We9kEVklIqtF5KqB7NOYZLng4+O9DsHkIUuijckBqk5loAxgJDriqM9fyVvVp3L0R3fx+r0/2TvKnQK9Jf9bKqYwqmlFyvadzUTk68DDwP+6i0YBjw1ws8uAM4GXetmvH7gZOAU4FDhXRA4d4H6NMSYrWRJtTA7QHsoi+kN8PmZceg+Lyudw9NobeevmC2htbhzwdmPRXmq5gyNmsB872Vq7JiX7znKXAscAewBU9QNgv4FsUFVXquqqOKsdBaxW1bWq2g48CMwdyH6NMSZbWRJtTA7YWxaRnIZPgYJCDr/8EV4fcR6zdvyDut8dybvzH076qHTn9mK05ht80GwANr7zXFL3mSPa3CQWABEJkJ5GBaOA6PnYa91l+xCRi0VkoYgsrKurS0NoxhiTXpZEZ7ja1ctYs/QNr8MwGS5ZNdHRfH4/R/+/P7L0hHvxEeKwFy9i9a+O4s2//Z76HVuTso/eRqInHnYMO6mED55Jyr5yzIsi8kOgREQ+BfwN+Ge8F4nIcyKyLMYt0dHkWP+lxUzeVfVWVZ2pqjOHDh2a4OaN6Ru1HnfGQ9adI8ONvu8Y587Uem8DMRmtt2R0oKYeO5f2WZ/mrSf+zLDltzNr+S8ILvsf3i84gJ1DjsA/8jCqxkxm2ITJVFYN7tPU4+HOGQv3Tf79gQBrqo5mUv2rBDva0zodeRa4CrgIWAr8P2AecHu8F6nqiQPcby0wJurxaGDTALdpjDFZKW+S6PfffoH25j1eh9FnU9yv4VAo5e3GTBbroctFshQWFXPUWZejZ3yb1Utfo+6th6natoAZW/5G4dYH4B1nvTYtYJdU0RAYRKu/nKC/hJC/hHCgmHCgFPyFqC+A+AKoL4CvcXOvcQcmn071a0+z+IWHmf6pL6XkvWUjd8KV29xbOi0AJonIBOAj4BzAfjDGmLyUN0m0/1+XMyW01usw+m3H1o0MHTne6zBMhkrGZCuJEJ+PA6bN5oBpTr1ye1srH65/j50fLqNt22po3Ia/ZQdFbTsoDDZSGtxNUbiVQm2jmFYKtQM/YQok1LnNdg1QOTT25HtT5nyeutd+jCy6EyyJTikROQP4IzAU+JeILFbVT4vISOB2VT1VVYMichnwNOAH7lRVmxHHeCYJDYmM6be8SaLDp93IipbUdBhIpfql8zh6073sqP3AkmjTo94u0EulwqJixh00nXEHTe/T6zQcJhwOEwy2I+JjXFFxzPUKCotYPe6LHP3hLbz31rMcfNSnkhC1iUVVHwUejbF8E3Bq1ON5OOUjxhiT1/ImiZ40/RNeh9AvHw4aBg/eS+OWNYAlECY2Vbe2OIkXFqaS+Hz4fT78gfiHoGmf/yF11z5E0VPfpWXyK5SUVaQhQmOMMaZ31p0jww0beyAAHTvWeRyJyWiRmugcPLdZWl7F5uOuZVzoQ1bddDYtTQ1eh5SRRORir2MwJt2sO4fxUt6MRGer4tJytlONf8/G+CunWLCjndaWJkId7XR0tBMOBQkF2wl1dBAKBQmHOggHOwgFO9ix9GkItnP0Rdd6HXZeiLS48yVhspVMdNjxZ/Nm3TqOXP5LNl17JFtmXM6UE79CcUmZ16Flktz7D8oYYzKYJdFZYEdgOKXNH/X79RoOU79zG7u2baRx+ybadm8m1LyLcGs90roHX1s9gY4GCjoaKAw1E9B2CrSNwnA7hbRRqB0U0U6BhCjv894tiU6HcCgIgC+H28DN+sKVLHv5QCrn/4CZi75H28IfsbJwEo1l4wiVj4TiSqSwFF9hGVJQiCAgfkSc8hHFh4g4F1+KJOWKpOGTjmD42ElJeHcDp6r/G38tY4wxyWJJdBZoKBnF6IYlaDjcYyuw1pYmNq56m/ralXTUrSGwez3lzRsY1LGNwbqLaglRHeN1LVpIo5TR7Cun1VdGu7+UZv9gwv4iwr4iwoFiNFCM+ougoAQJFEGgCPEFEH8B+APu/QA+fwHiL0D8AUpev46Dgqusv2+aaCSJ9uf2n/SUT8wl/PHPsvTVJ2ha/iRVO99l7O63qNm1E7+k/7zuW7t/zvCx30nb/kTkv3p7XlWvS1csxhiT71L+iSsiJwN/wGmHdLuqXtPteXGfPxVoBi5Q1bdTHVc2CY05muErnuf9xS9z4IzjaGttZt2y19n9/uv4t75LTcMqxoQ2MEn2Tsm8jcHUFY5iY9VM1pXth5QPo6B6BCWDRlI+ZATl1UMprxpCSVExJSmI+c26dfDeNdTv3MaQYaNTsAcTTcMdAPj8BR5Hkno+v5+px86FY/dOshcKBmlsbqC1uYG25gaC7W2giqJoOIQq7ld1JqZJUiHlAaMmJmU7fWBXVRpjTIZIaRItTquAm3HaStQCC0TkcVVdEbXaKcAk9zYL+LP71bgOPvECdq64keGPn8u6J2oYFfqIg8UZedxONR8VT2LBkOMpGj2NweOnMmzsQexXVsF+HsYcqHCm+W3YuSVrk+hgRzuhUHBv+7iIqDKA7hfyRfdplh7WS2Qd6NvEKZFyDn+Oj0T3xB8IUF45iPLKQV6HklKq+nOvYzDGGONI9SfuUcBqVV0LICIPAnOB6CR6LnCvqirwhohUi8gIVd2c4tiyRtXgoaw9+2F2PvNb/MFmtlbOpmjCLEZNOZb9Rk2gxusAYygftj8AW5+/mbpVR7hLI0mi4vy4FdG99+m8T+fj7l+1y7Ke13NGISOrhN19gRI1ChljW6KKr3Ezk3a/wiD2ZGS9U1j3JtuRyA8HEPAXFHkRkkkzETkQZ8BhmKpOEZHDgNNV9X88Ds0YY/JGqnOEUUB0W4la9h1ljrXOKKBLEu22b7oYYOzY2LOb5bL9p8xi/yl/9zqMhB0w/Vjee/pQZm1/BLY/4nU4fdKihSyvOpaO6onO9NTOlWnOk9ptVHpvqk50Sa4StV5vpQNdntO4y6XbtjT6udLBzNr/0J73ZXLJbcCVwP8CqOq7IvJXwJJok1eOGOecfTr2wEwcTjK5LtVJdKzL37tnFImsg6reCtwKMHPmTOsMmeH8gQAHXvUKWzevJ9TR4Y4gh4m0Jhe3O4LzxefedzonRMoaBAGfOPe6Pd9Z+tDltd1v3ffV9fVd9iXSWT5RAsxM4/fKmH4oVdW3upUABb0KxhivTBtTzar/OZmiQG629zSZLdVJdC0wJurxaGBTP9YxWcjn9zNsdNovvDImH2wXkYm4Aw4icjbdzt4Zky8sgTZeSfWMhQuASSIyQUQKgXOAx7ut8zhwnjg+BtRbPbQxxvTqUpxSjoNF5CPgO8AlnkZkjDF5JqUj0aoaFJHLgKdxWtzdqarLReQS9/lbgHk47e1W47S4uzCVMRljTLZzL9Y+UUTKcAZDWoAvAh96GpgxxuSRlDcfUNV5OIly9LJbou4rzqiKMcaYXohIJc7xchTwD+A59/F3gSXA/d5FZ4wx+SUTO3gZY4yJ7S/ALuB14OvA94BC4HOqutjDuIwxJu9YEm2MMdljf1WdCiAitwPbgbGq2uBtWMYYk39EkzT9bTqJSB39q/2rwfnQyTYWd3pZ3OmXrbH3J+5xqjq0PzsTkbdVdUZPjzOVHbOzRrbGDdkbu8WdXv2NO+ZxOyuT6P4SkYWqmnUtgC3u9LK40y9bY0933CISApoiD3Hamje791VVK9MVSzrY70V6ZWvckL2xW9zpley4rZzDGGOyhKpaQ1xjjMkQqe4TbYwxxhhjTM7JtyT6Vq8D6CeLO70s7vTL1tizNe5ska3fX4s7/bI1dos7vZIad17VRBtjjDHGGJMM+TYSbYwxxhhjzIDlRRItIieLyCoRWS0iV3kdTzQRGSMi80VkpYgsF5Fvu8sHi8izIvKB+3VQ1Gt+4L6XVSLyae+iBxHxi8g7IvKE+zjj4xaRahF5WETec7/vR2dJ3Je7vyPLROQBESnO1LhF5E4R2SYiy6KW9TlWETlCRJa6z90oIuJB3L9zf1feFZFHRaQ60+LORZl63LZjtjfsuJ3yOO2Y3Z+4VTWnb4AfWAPsjzOz1xLgUK/jiopvBDDDvV8BvA8cCvwWuMpdfhXwG/f+oe57KAImuO/N72H8/wX8FXjCfZzxcQP3AF9z7xcC1ZkeN840z+uAEvfx/wEXZGrcwLHADGBZ1LI+xwq8BRyN08LtSeAUD+I+CQi493+TiXHn2i2Tj9t2zPYsbjtupzZWO2b3I+58GIk+ClitqmtVtR14EJjrcUydVHWzqr7t3m8AVuL84c3FOWjgfv2ce38u8KCqtqnqOmA1zntMOxEZDXwGuD1qcUbHLSKVOH90dwCoaruq7ibD43YFgBIRCQClwCYyNG5VfQnY2W1xn2IVkRFApaq+rs5R7t6o16QtblV9RlWD7sM3gNGZFncOytjjth2z08+O26lnx+z+xZ0PSfQoYGPU41p3WcYRkfHA4cCbwDBV3QzOQRvYz10tk97PDcD3gHDUskyPe3+gDrjLPaV5u4iUkeFxq+pHwLXABmAzUK+qz5DhcXfT11hHufe7L/fSV3FGKSC74s42mfj7uw87ZqeNHbe9YcfsOPIhiY5V15JxLUlEpBz4O/AdVd3T26oxlqX9/YjIZ4Ftqroo0ZfEWObFzyGAc+rnz6p6OM7sb73VW2ZE3G4t2lycU1AjgTIR+UpvL4mxLON+7109xZpR70FEfgQEgfsji2KslnFxZ6mM/x7aMTut7LidWbLi2JeOY3Y+JNG1wJiox6NxTqdkDBEpwDkY36+qj7iLt7qnGHC/bnOXZ8r7OQY4XUTW45xqPUFE7iPz464FalX1TffxwzgH50yP+0RgnarWqWoH8AjwcTI/7mh9jbWWvafhopennYicD3wW+LJ7ug+yIO4slom/v53smJ12dtz2hh2z48iHJHoBMElEJohIIXAO8LjHMXVyrwC9A1ipqtdFPfU4cL57/3zgH1HLzxGRIhGZAEzCKYhPK1X9gaqOVtXxON/Tf6vqV8j8uLcAG0XkIHfRJ4EVZHjcOKcDPyYipe7vzCdxajEzPe5ofYrVPX3YICIfc9/zeVGvSRsRORn4PnC6qjZHPZXRcWe5jD1u2zE7/ey47Rk7ZscT78rDXLgBp+JcQb0G+JHX8XSLbTbOaYN3gcXu7VRgCPA88IH7dXDUa37kvpdVZMBV/8Ac9l7pnfFxA9OBhe73/DFgUJbE/XPgPWAZ8BecK4wzMm7gAZwawA6c//Iv6k+swEz3/a4BbsKdICrNca/GqaOL/H3ekmlx5+ItU4/bdsz2LGY7bqc2Tjtm9yNum7HQGGOMMcaYPsqHcg5jjDHGGGOSypJoY4wxxhhj+siSaGOMMcYYY/rIkmhjjDHGGGP6yJJoY4wxxhhj+siSaGOMMcYYY/rIkmiTVURkiIgsdm9bROQj936jiPwpBfu7W0TWicglSdjW79yYv5uM2IwxJtPZMdvksoDXARjTF6q6A6fpPiJyNdCoqtemeLdXqurDA92Iql4pIk3JCMgYY7KBHbNNLrORaJMTRGSOiDzh3r9aRO4RkWdEZL2InCkivxWRpSLylIgUuOsdISIvisgiEXlaREYksJ+7ReTPIjJfRNaKyHEicqeIrBSRu911/O56y9x9Xp7SN2+MMVnGjtkmF1gSbXLVROAzwFzgPmC+qk4FWoDPuAflPwJnq+oRwJ3ALxPc9iDgBOBy4J/A9cBkYKqITMcZdRmlqlPcfd6VrDdljDE5yo7ZJutYOYfJVU+qaoeILAX8wFPu8qXAeOAgYArwrIjgrrM5wW3/U1XV3fZWVV0KICLL3W2/COwvIn8E/gU8k5R3ZIwxucuO2SbrWBJtclUbgKqGRaRDVdVdHsb5vRdguaoe3d9tu9tqi1oeBgKquktEpgGfBi4FvgB8tR/7McaYfGHHbJN1rJzD5KtVwFARORpARApEZHIyNiwiNYBPVf8O/ASYkYztGmNMHrNjtsk4NhJt8pKqtovI2cCNIlKF87dwA7A8CZsfBdwlIpF/Un+QhG0aY0zesmO2yUSy94yJMaY79+rtJ5LRLsnd3tWkp8WTMcbkHTtmm3Sycg5jelcP/HeyGvcDXwGs76gxxqSGHbNN2thItDHGGGOMMX1kI9HGGGOMMcb0kSXRxhhjjDHG9JEl0cYYY4wxxvSRJdHGGGOMMcb0kSXRxhhjjDHG9NH/B1svr2F2elqMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_T, label=\"NEST\")\n",
    "plt.plot(cr.times, cr.I_T, label=\"Control\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"I_T [mV]\")\n",
    "plt.title(\"I_T current\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_T - cr.I_T) / np.abs(cr.I_T))\n",
    "plt.title(\"Relative I_T error\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel. error (NEST-Control)/|Control|\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Also here the results are in good agreement and the error appears acceptable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I_NaP channel\n",
    "\n",
    "This channel adapts instantaneously to changes in membrane potential:\n",
    "\n",
    "\\begin{align}\n",
    "I_{NaP} &= - g_{\\text{peak}, NaP} (m_{NaP}^{\\infty}(V, t))^3 (V-E_{NaP}) \\\\\n",
    "m_{NaP}^{\\infty}(V) &= \\frac{1}{1+\\exp\\left(-\\frac{V+55.7\\text{mV}}{7.7\\text{mV}}\\right)}\n",
    "\\end{align}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class INaP(Channel):\n",
    "    nest_g = \"g_peak_NaP\"\n",
    "    nest_I = \"I_NaP\"\n",
    "\n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1 / (1 + np.exp(-(V + 55.7) / 7.7))\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        return self.I_V_curve(V * np.ones_like(t))\n",
    "\n",
    "    def I_V_curve(self, V):\n",
    "        self.m = self.m_inf(V)\n",
    "        return -self.hp[\"g_peak_NaP\"] * self.m**3 * (V - self.hp[\"E_rev_NaP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "iNaP = INaP(nest.GetDefaults(\"ht_neuron\"))\n",
    "V = np.arange(-110.0, 30.0, 1.0)\n",
    "nr, cr = voltage_clamp(iNaP, [(1, v) for v in V], nest_dt=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nr.times, nr.I_NaP, label=\"NEST\")\n",
    "plt.plot(cr.times, cr.I_NaP, label=\"Control\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"I_NaP [mV]\")\n",
    "plt.title(\"I_NaP current\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nr.times, (nr.I_NaP - cr.I_NaP))\n",
    "plt.title(\"I_NaP error\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Error (NEST-Control)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Perfect agreement\n",
    "- Step structure is because $V$ changes only every second."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I_KNa channel (aka I_DK)\n",
    "\n",
    "Equations for this channel are\n",
    "\n",
    "\\begin{align}\n",
    "I_{DK} &= - g_{\\text{peak},DK} m_{DK}(V,t) (V - E_{DK})\\\\\n",
    "m_{DK} &= \\frac{1}{1 + \\left(\\frac{d_{1/2}}{D}\\right)^{3.5}}\\\\\n",
    "\\frac{dD}{dt} &= D_{\\text{influx}}(V) - \\frac{D-D_{\\text{eq}}}{\\tau_D} = \\frac{D_{\\infty}(V)-D}{\\tau_D} \\\\\n",
    "D_{\\infty}(V) &= \\tau_D D_{\\text{influx}}(V) + {D_{\\text{eq}}}\\\\\n",
    "D_{\\text{influx}} &= \\frac{D_{\\text{influx,peak}}}{1+ \\exp\\left(-\\frac{V-D_{\\theta}}{\\sigma_D}\\right)} \n",
    "\\end{align}\n",
    "\n",
    "with \n",
    "\n",
    "|$D_{\\text{influx,peak}}$|$D_{\\text{eq}}$|$\\tau_D$|$D_{\\theta}$|$\\sigma_D$|$d_{1/2}$|\n",
    "| --: | --: | --: | --: | --: | --: |\n",
    "|$0.025\\text{ms}^{-1}$ |$0.001$|$1250\\text{ms}$|$-10\\text{mV}$|$5\\text{mV}$|$0.25$|\n",
    "\n",
    "Note the following:\n",
    "- $D_{eq}$ is the equilibrium value only for $D_{\\text{influx}}(V)=0$, i.e., in the limit $V\\to -\\infty$ and $t\\to\\infty$.\n",
    "- The actual steady-state value is $D_{\\infty}$.\n",
    "- $m_{DK}$ is a steep sigmoid which is almost 0 or 1 except for a narrow window around $d_{1/2}$.\n",
    "- To the left of this window, $I_{DK}\\approx 0$.\n",
    "- To the right of this window, $I_{DK}\\sim -(V-E_{DK})$.\n",
    "- $m_{DK}$ is not integrated over time, instead it is an instantaneous transform of $D$, which is integrated over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class IDK(Channel):\n",
    "    nest_g = \"g_peak_KNa\"\n",
    "    nest_I = \"I_KNa\"\n",
    "\n",
    "    def __init__(self, ht_params):\n",
    "        self.hp = ht_params\n",
    "\n",
    "    def m_DK(self, D):\n",
    "        return 1 / (1 + (0.25 / D) ** 3.5)\n",
    "\n",
    "    def D_inf(self, V):\n",
    "        return 1250.0 * self.D_influx(V) + 0.001\n",
    "\n",
    "    def D_influx(self, V):\n",
    "        return 0.025 / (1 + np.exp(-(V + 10) / 5.0))\n",
    "\n",
    "    def dD(self, D, t, V):\n",
    "        return (self.D_inf(V) - D) / 1250.0\n",
    "\n",
    "    def compute_I(self, t, V, m0, h0, D0):\n",
    "        self.D = si.odeint(self.dD, D0, t, args=(V,))\n",
    "        self.m = self.m_DK(self.D)\n",
    "        return -self.hp[\"g_peak_KNa\"] * self.m * (V - self.hp[\"E_rev_KNa\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Properties of I_DK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "iDK = IDK(nest.GetDefaults(\"ht_neuron\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = np.linspace(0.01, 1.5, num=200)\n",
    "V = np.linspace(-110, 30, num=200)\n",
    "\n",
    "ax1 = plt.subplot2grid((1, 9), (0, 0), colspan=4)\n",
    "ax2 = ax1.twinx()\n",
    "ax3 = plt.subplot2grid((1, 9), (0, 6), colspan=3)\n",
    "\n",
    "ax1.plot(V, -iDK.m_DK(iDK.D_inf(V)) * (V - iDK.hp[\"E_rev_KNa\"]), \"g\")\n",
    "ax1.set_ylabel(\"Current I_inf(V)\", color=\"g\")\n",
    "ax2.plot(V, iDK.m_DK(iDK.D_inf(V)), \"b\")\n",
    "ax2.set_ylabel(\"Activation m_inf(D_inf(V))\", color=\"b\")\n",
    "ax1.set_xlabel(\"Membrane potential V [mV]\")\n",
    "ax2.set_title(\"Steady-state activation and current\")\n",
    "\n",
    "ax3.plot(D, iDK.m_DK(D), \"b\")\n",
    "ax3.set_xlabel(\"D\")\n",
    "ax3.set_ylabel(\"Activation m_inf(D)\", color=\"b\")\n",
    "ax3.set_title(\"Activation as function of D\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that current in steady state is \n",
    "- $\\approx 0$ for $V < -40$mV\n",
    "- $\\sim -(V-E_{DK})$ for $V> -30$mV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Voltage clamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "nr, cr = voltage_clamp(iDK, [(500, -65.0), (500, -35.0), (500, -25.0), (500, 0.0), (5000, -70.0)], nest_dt=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot2grid((1, 9), (0, 0), colspan=4)\n",
    "ax2 = plt.subplot2grid((1, 9), (0, 6), colspan=3)\n",
    "\n",
    "ax1.plot(nr.times, nr.I_KNa, label=\"NEST\")\n",
    "ax1.plot(cr.times, cr.I_KNa, label=\"Control\")\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax1.set_xlabel(\"Time [ms]\")\n",
    "ax1.set_ylabel(\"I_DK [mV]\")\n",
    "ax1.set_title(\"I_DK current\")\n",
    "\n",
    "ax2.plot(nr.times, (nr.I_KNa - cr.I_KNa) / np.abs(cr.I_KNa))\n",
    "ax2.set_title(\"Relative I_DK error\")\n",
    "ax2.set_xlabel(\"Time [ms]\")\n",
    "ax2.set_ylabel(\"Rel. error (NEST-Control)/|Control|\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks very fine.\n",
    "- Note that the current gets appreviable only when $V>-35$ mV\n",
    "- Once that threshold is crossed, the current adjust instantaneously to changes in $V$, since it is in the linear regime.\n",
    "- When returning from $V=0$ to $V=-70$ mV, the current remains large for a long time since $D$ has to drop below 1 before $m_{\\infty}$ changes appreciably"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Synaptic channels\n",
    "\n",
    "For synaptic channels, NEST allows recording of conductances, so we test conductances directly. Due to the voltage-dependence of the NMDA channels, we still do this in voltage clamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class SynChannel:\n",
    "    \"\"\"\n",
    "    Base class for synapse channel models in Python.\n",
    "    \"\"\"\n",
    "\n",
    "    def t_peak(self):\n",
    "        return self.tau_1 * self.tau_2 / (self.tau_2 - self.tau_1) * np.log(self.tau_2 / self.tau_1)\n",
    "\n",
    "    def beta(self, t):\n",
    "        val = (np.exp(-t / self.tau_1) - np.exp(-t / self.tau_2)) / (\n",
    "            np.exp(-self.t_peak() / self.tau_1) - np.exp(-self.t_peak() / self.tau_2)\n",
    "        )\n",
    "        val[t < 0] = 0\n",
    "        return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def syn_voltage_clamp(channel, DT_V_seq, nest_dt=0.1):\n",
    "    \"Run voltage clamp with voltage V through intervals DT with single spike at time 1\"\n",
    "\n",
    "    spike_time = 1.0\n",
    "    delay = 1.0\n",
    "\n",
    "    nest.ResetKernel()\n",
    "    nest.resolution = nest_dt\n",
    "    try:\n",
    "        nrn = nest.Create(\n",
    "            \"ht_neuron\", params={\"theta\": 1e6, \"theta_eq\": 1e6, \"instant_unblock_NMDA\": channel.instantaneous}\n",
    "        )\n",
    "    except:\n",
    "        nrn = nest.Create(\"ht_neuron\", params={\"theta\": 1e6, \"theta_eq\": 1e6})\n",
    "\n",
    "    mm = nest.Create(\"multimeter\", params={\"record_from\": [\"g_\" + channel.receptor], \"interval\": nest_dt})\n",
    "    sg = nest.Create(\"spike_generator\", params={\"spike_times\": [spike_time]})\n",
    "    nest.Connect(mm, nrn)\n",
    "    nest.Connect(sg, nrn, syn_spec={\"weight\": 1.0, \"delay\": delay, \"receptor_type\": channel.rec_code})\n",
    "\n",
    "    # ensure we start from equilibrated state\n",
    "    nrn.set(V_m=DT_V_seq[0][1], equilibrate=True, voltage_clamp=True)\n",
    "    for DT, V in DT_V_seq:\n",
    "        nrn.set(V_m=V, voltage_clamp=True)\n",
    "        nest.Simulate(DT)\n",
    "    t_end = nest.biological_time\n",
    "\n",
    "    # simulate a little more so we get all data up to t_end to multimeter\n",
    "    nest.Simulate(2 * nest.min_delay)\n",
    "\n",
    "    tmp = pd.DataFrame(mm.get(\"events\"))\n",
    "    nest_res = tmp[tmp.times <= t_end]\n",
    "\n",
    "    # Control part\n",
    "    t_old = 0.0\n",
    "    t_all, g_all = [], []\n",
    "\n",
    "    m_fast_old = channel.m_inf(DT_V_seq[0][1]) if channel.receptor == \"NMDA\" and not channel.instantaneous else None\n",
    "    m_slow_old = channel.m_inf(DT_V_seq[0][1]) if channel.receptor == \"NMDA\" and not channel.instantaneous else None\n",
    "\n",
    "    for DT, V in DT_V_seq:\n",
    "        t_loc = np.arange(0.0, DT + 0.1 * nest_dt, nest_dt)\n",
    "        g_loc = channel.g(t_old + t_loc - (spike_time + delay), V, m_fast_old, m_slow_old)\n",
    "        t_all.extend(t_old + t_loc[1:])\n",
    "        g_all.extend(g_loc[1:])\n",
    "        m_fast_old = channel.m_fast[-1] if m_fast_old is not None else None\n",
    "        m_slow_old = channel.m_slow[-1] if m_slow_old is not None else None\n",
    "        t_old = t_all[-1]\n",
    "\n",
    "    ctrl_res = pd.DataFrame({\"times\": t_all, \"g_\" + channel.receptor: g_all})\n",
    "\n",
    "    return nest_res, ctrl_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AMPA, GABA_A, GABA_B channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "\n",
    "\n",
    "class PlainChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp[\"receptor_types\"][receptor]\n",
    "        self.tau_1 = hp[\"tau_rise_\" + receptor]\n",
    "        self.tau_2 = hp[\"tau_decay_\" + receptor]\n",
    "        self.g_peak = hp[\"g_peak_\" + receptor]\n",
    "        self.E_rev = hp[\"E_rev_\" + receptor]\n",
    "\n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        return self.g_peak * self.beta(t)\n",
    "\n",
    "    def I(self, t, V):\n",
    "        return -self.g(t) * (V - self.E_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ampa = PlainChannel(nest.GetDefaults(\"ht_neuron\"), \"AMPA\")\n",
    "am_n, am_c = syn_voltage_clamp(ampa, [(25, -70.0)], nest_dt=0.1)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(am_n.times, am_n.g_AMPA, label=\"NEST\")\n",
    "plt.plot(am_c.times, am_c.g_AMPA, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_AMPA\")\n",
    "plt.title(\"AMPA Channel\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(am_n.times, (am_n.g_AMPA - am_c.g_AMPA) / am_c.g_AMPA)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"AMPA rel error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks quite good, but the error is maybe a bit larger than one would hope.\n",
    "- But the synaptic rise time is short (0.5 ms) compared to the integration step in NEST (0.1 ms), which may explain the error.\n",
    "- Reducing the time step reduces the error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvD1Py/WJiIiI5KOwE+yNwMiU7RG8d5hHa2U+ALzt7jvcvQF4GDil+QXc/XZ3n+bu0wYN6vjyd9nkNbtp8Cg9e/XtdB3jppzMbnpja5/NXGAiIiIikjFhJ9jzgQlmNtbMykhOUpzTrMwc4MpgNZGTSA4F2UJyaMhJZlZlZgacA6wIOd5QRev2UG29OvSY9OYi0Shre09n/L5XScTjGYxORERERDIh1ATb3RuB64GnSCbHD7r7MjO71syuDYrNBdYCq4E7gM8F574C/B54HXgjiPX2MOMNW0l9NQcivbpcj48/h37sY80bL2UgKhERERHJpJKwL+Duc0km0an7bkt578B1rZz7LeBboQaYReUNe6mN9u5yPWNPvAgWfp1di/7EhGNPz0BkIiIiIpIpepJjFlU17qOutE+X6xk4ZCRrouPovfn5DEQlItK+NB4adqaZVZvZouD1X7mIU0QkHyjBzqIeiX00lPXNSF3bDzuNCfXL2V+9OyP1iYi0Js2HhgH83d2PDV7/ndUgRUTyiBLsLOrt+0lU9MtIXb2mnEepxVn9ytz2C4uIdE06Dw0TEZGAEuwsqT24nwprwCszk2AfMe1caryc2MpnMlKfiEgb0nloGMDJZrbYzP5kZpOzE5qISP5Rgp0l+/Ykn+IY7TEgI/WVlVfwVtVxjNj9ckbqExFpQzoPDXsdGO3uU4GbgUdbrayAHhAmItIZSrCz5MCe5C+Rkp79M1Zn/egzGe7b2Lh6acbqFBFpQbsPDXP3fe5+IHg/Fyg1s4EtVVZIDwgTEekMJdhZUlud7MEu75W5XybDpn0IgE2vPZ6xOkVEWtDuQ8PMbEjwUDDMbDrJ3y+7sh6piEgeUIKdJQ0H9wBQ0TszQ0QARoybzGYbTPn6v2WsThGR5tJ8aNglwFIzWwzcBFwWPOdARKTbCf1BM5LUULMPgMqefTNWp0UibOh/MlN2Pkmsvo6y8oqM1S0ikiqNh4b9HPh5tuMSEclH6sHOkkRtNQA9emdmFZEmZRPPpYfVsWrBXzJar4iIiIh0jhLsLPH6/QBU9eqb0XoPP/FCGjzKvmVPZrReEREREekcJdhZYnXV1HoZpWXlGa23V5/+rCo7ikHbXsxovSIiIiLSOUqwsyTScICDVhVK3dXDT+fw+Bp2bt3QfmERERERCZUS7CyJxvZTG1KCPXDqhQCse1XL9YmIiIjkmhLsLCltPEhttGcodY8/5lT20Btf82wo9YuI5But/yci+UwJdpaUNR4gFg2nBzsSjbKm1wmMq36FRDweyjVERPKBtfTQdhGRPKMEO0sqEgeJlfQK7wITzmUA1axe/PfwriEiIiIi7VKCnSWViYPES8MZIgIw4dSP0ugRdr32aGjXEBEREZH2KcHOkiqvIV4WXg92nwGDWVk+mSFbNA5bREREJJeUYGdBIh6nB3UQYoINsH/0eYxNrGfT2hWhXkdE8o+ZRc3sxlzHISIiSrCz4uCBaiLmUNE71OuMPPmfANgw7w+hXkdE8o+7x4H3mWkaoIhIroWeYJvZBWa20sxWm9kNLRw3M7spOL7EzI5POdbXzH5vZm+a2QozOznseMNwcN9uACIhJ9jDx01mXWQkPdc/E+p1RCRvLQT+aGYfN7OPNr1yHZSISHfTqQTbzMab2TfNbGk75aLALcAMYBIwy8wmNSs2A5gQvGYDt6Yc+xnwpLsfCUwFCnLsQ92BvQBEq/qEfq0tQ87iyLolVO/eEfq1RCTv9Ad2AWcDFwWvD+U0IhGRbqgk3YJmNhT4Z+By4Bjg/wCz2jltOrDa3dcGddwPzASWp5SZCdzj7g7MC3qthwIHgTOAqwHcPQbE0o03n9Tt3wNAaWX4CXa/4y+mZPM9rHrxYaZd9JnQryci+cPdr8l1DCIikkYPtpl92syeBZ4DBgKfAra4+3fc/Y12Th8ObEjZ3hjsS6fMOGAHcJeZLTSzX5pZjxbim21mC8xswY4d+dlrG6vZB0BZVbhDRACOOO5MdtEHe+tPoV9LRPKLmY0ws0fMbLuZbTOzP5jZiFzHJSLS3aQzROQWIApc7u7fdPclpP+U2pYm2zQ/t7UyJcDxwK3ufhzJHu33jOF299vdfZq7Txs0aFCaYWVXvP4AAKVZSLAj0Shr+p3GEfvmEauvC/16IpJX7gLmAMNIdlQ8FuwTEZEsSifBHgbcD/w4mKz4XaA0zfo3AiNTtkcAm9MssxHY6O6vBPt/TzLhLjiNtckEu7wyvAfNpCo/+sP0slpWvDgnK9cTkbwxyN3vcvfG4PVrID97HkREili7Y7DdfSfJiYe3Bl81XgZsN7MVwCPu/o02Tp8PTDCzscCm4NzLm5WZA1wfjM8+Eah29y0AZrbBzCa6+0rgHA4du10wErGDAFRUhbsOdpOjTr2Yfc9/mdiSh+Hsj2XlmiKSF3aa2RXAfcH2LJKTHkVEQre3Jsax/53eSmb3ffokpo/tTzTyj4EML63eycQhvfjtK+/w42feenf/mAFV3DzreL74wELKohG+fO4RnD95yCH1vbOrhudX7eDxJZuZt3Z3WjHc84npPLFkC5VlUb794clpnZOutCY5mtnFwOHAG+7+I+BHZjaRZMLcKndvNLPrgadIDjO5092Xmdm1wfHbgLnAhcBqoAZInaTzeeC3ZlYGrG12rGB4U4LdM/whIgBl5RUs7nM6E/c+T6y+jrLyiqxcV0Ry7hPAz4GfkBxq91KwT0QKWKwxQW1DnMrSKCURIxLp2nL3jfEE0YixpbqOV9/ezZceWMSj153KgB5lrNt1kKeWbaUx7jTEndlnjOPlNTupaYgzuFcFpx8xkPJolN6VJTQtu19d08C07z9DQzzdEcQw6455aZddt6uGi37+wrvbn/nNa+nfbBuuvPPVd99/66JJ795PJrSbYJvZL4DJJBvq75rZdHf/btCr/J32znf3uSST6NR9t6W8d+C6Vs5dBExr7xr5zuuTCXZllnqwAUqP+Si9//4Ui196jKlnXZq164pIbgTLov7A3T+c61hE5L3W7jjAF+5fyNJN+w7Z/5tPTmdon0pGD6iiNBqhriFOPOGURI0Nu2v4wI+fz0p8F9/yYov7//D6xqxcP9cy/YyudHqwzwCmunvczKqAvwPfzWgURc4aaqjzUipK0l4VscuOOvXD7H/+S9QvfhiUYIsUvaCNHmRmZcGypiKSA1+8fyF/XNR8ulnrPv6rV9svJAUnnYwvFjyCF3ev0WN4O84aDlJrlWRzoEZ5RRVL+p7OxL3PaZiISPexDnjRzOaQXHkJAHf/cc4iCsmB+oZch1A0VmzZx4h+lVSVlfDXN7fzqXsWHHJ83Q8/mKPICsun7p7Pn1dsz3UYOTfv6+cwpE96OcfugzGeXLqVbzySXPX5WxdNYs7izXz69HFcePTQQ8oerG+kqiyKmfHa+j38060vHXL8mx88ikunjaRHWZSSaHrPUdxzMEZZSYQe5ZnvAE2nxiPNbEnw3oDxwbaRHOFxTMajKjKRxhrqKM/6dUuP/ih9Xnhaw0REuo/NwSsCZHRMmpldQPLpulHgl+7+w2bHLTh+Icn5NFe7++uZjAFgf10jAF9+YDEfOS6cJb4XrNvNa+v30JhwbnxqJU984TTW7DhIdU2MK04a3eZXyYmEvzs+tr4xTnlJlOWb9zGsbwWxxgRb99VxzIi+XY4xkXAaE05ZSYTFG/Yys9nX+9//yBQWvrOXjxw3nFMPH0g84dz14tt874mOPxB5zA1PvGffh44Zyk//+Vi+98QKHl20iYX/eW6L/13+vHwbn7pnAZ85Yxzz1+3m9Xf2AnDMiD4s2Vh9SFkzuPeTJ3LSuAGHTHxrjCfYdTDG4N4VxBPOii372FMT4+RxA9JOpLoiOZIV6hsTvLBq53v+AMmGP3z2ZN43uv8h+w7UN3Luj59jS3Udc79wOiP7V/LCqp08sGADx4/qxytv7+I7H55Mv6oyDtQ38v4b/wbAI587hSMG9wolqeyo/j3KuPzEUVx+4qh3911z6tgWy6bG+77R/TLyh1+/HmVdrqM11vQ/TqsFzEa3ddzd12c0oi6YNm2aL1iQ/f/x2/P6jR+if+06xvxXm0+Wz7j6uhpi/2ccK/qdxfQv3df+CSKSU2b2mrt3at5JMAb7bne/IsNhNdX9FnAuySVU5wOz3H15SpkLSU5Mv5DkilA/c/cT26u7o+32S2t2cvkdydVbF3/rPPpUtrxqbCLhrNi6j1+98DYXThnK5377OqdPGMizK7fTzq+9tAzrU8Hm6uw8a2BAjzKOH92PZ5Zvy8r1Cs3TXz6DIwan//dkPOGc95PnWLPjYPuFO2nuF05n2/46rrlrPv967hF8/pwJaZ3XEE9QmoU/GiQz2mqz01mmL28S6EJVEq8lFqnM+nWbhokcufc56utqKK+oynoMIpIdIY/Bng6sdve1AMGyqjM5dOnUmcA9wcT1eWbW18yGNi27mjEpyfHU7zyd1ikPv74JgL+8mbmv77OVXAPsOhgLNbl+5stnsGLrfnYfqKeiNMrHpo18txd+6aZqPnTzC+3UkFvn/SQ7kwDTsfQ759Mz6GmdRO8O97IquS4e6awisp9Dn75owXbTEJHsrD1XwErjNTTkIMEGKD/uMnr/7WkWPvcQx51/VU5iEJGsWUc4Y7CHAxtStjeS7KVur8xwILMJdgZNH9ufV99Orpd706zjmDFlCAZEI9bicIe6hjhH/ueTLdb1Xx+axNWnjHk3Md19MEafylL21zXQt6oMd8fMWhxuAfDlDxzB0SN6853HlrN+V02rMU8b3Y8bL53KK2t3kXDYV9fA7NPHHbJs28H6RhriCfpWlVHXEOe259YwfUx/Th4/oNXhLRNa6QGeMrxPi0niq2/vZsnGvVw0dRjlJZFW1z5+/POnMWV4H2KNCUqjyf+uLfXSbttXx5KN1by2fg+3Pbem1fuH5OcTT2Tga4hO+PHHpnLe5CHvJtFNowA0PU2aS2cAzl+AIcDDwP3u/k64IRWfskQd+8ty83fIpFMvYuff+sLiB0AJtkixC2sMdkvZQ/MMJ50yyYJms4HZAKNGjWqpSKvaSqvOmjiIH3/sWPpWlYaS8FSURtPukewfjO3sW5X82RRPe+effeTgtOofO7BHq8dSx6pWlEb50geOSKvOjpg+tj/Tx/5jTHB791VW8o+EuqVe2sG9Kzh3UgXnThrMDTOOTDuOTXtrOfWHz6Zdvkl5SYSV35sBJP9w2lJdx+j+VR1eX1qJtbQmnSEiF5tZH+CjwB1mVgE8QDLZTu9ROd1cWaKOeEluhmeUlJaxevAFHL/1Ifbu3ErfgUPaP0lECpK7fwfAzHq4eyYHmG4ERqZsjyCZyHe0TFOctwO3Q3IMdlcC0woX3dvwvpVd/n+gojTa5h8sIp2R1mAfd69297uAGcBtwH8DV4cYV1Gp8FriJbkZIgIw8JQrKbM4K5/9Tc5iEJHwmdnJZrYcWBFsTw0eFtZV84EJZjY2eLLuZcCcZmXmAFda0klAdcbHX8MhExSVXItIvkorwTazU8zsZuB14FTgI8W4rmpYKryORI56sAHGH30y6yKj6LPq4ZzFICJZ8VPgfGAXgLsvJvmwsC5x90bgeuApksn7g+6+zMyuNbNrg2JzgbXAauAO4HNdva6ISKFKZ5LjOmAvcD/JMXONwf7jAcJY57SYeCJBJfV4We6+frJIhC1jLubktTexae0yho+bnLNYRCRc7r6h2bjQeIbqnUsyiU7dd1vKeweuy8S1REQKXTqTHNeRnFdyPnAeh05kceDszIdVPOrra6mwBFaa2/Fd486+msSam3nnr3cxfNyPchqLiIRmg5mdAngwlOMLBMNFioW3Oc1RRCQ/pDPJ8czWjplZyyv8y7vqDu5PPiK9LLdrUA8eMZ43Ko5j7IZHiTf+kGhJ7p/gJCIZdy3JpykOJznp8GnUqywiknUdXtE8mMBytpn9kmQDLm2oq9kPQKS8Z44jgdjUKxnCDpb+/ZFchyIiIXD3ne7+L+4+2N0Pc/cr3H1XruPKpKZJjqcdPjC3gYiItCHtBNvMTjSznwHrSc4W/zuQ/mKV3VT9wX0ARCpyvwTQ0efMYje9SSz4da5DERHpEi0/LCL5rN0E28y+b2argB8AbwDHATvc/W533xN2gIWuvvYAACUVmXzmQ+eUlVewcuiHOfrAS+zcvD7X4YiIdJhGYItIIUinB3s2sA24Fbg3+LpRbVyaGpoS7PLcjsFuMuKcaymxBKuf/n+5DkVERESkKKUz020IydVDZgE/NbO/ApVmVhKsjSptaIzVAFCSB0NEAEYefjTLyqYyav3vScS/SyQazXVIItJFZvaVto4X03ML3NW/IyL5r90ebHePu/uf3P1K4HDgj8BLwCYz+13YARa6RJBgl5bnR4INUDv14wzzbSx9XpMdRYpEr3ZeRcc0CFtE8liH1mpz9zrg98Dvzaw38JGmY2Z2lbvfneH4Cl68vhaAsorcPSq9uaPP+Rd2zv8e/sptcNYluQ5HRLrI3b+T6xiyRf3XIlIIOrxMXxN339csof5iBuIpOomGZIJdmidDRADKK6pYNfoyptbNZ/3KRbkOR0QyxMyOMLO/mNnSYPsYM/tmruMKg/qvRSSfdTrBbkGL7Z2ZXWBmK81stZnd0MJxM7ObguNLmh7BnnI8amYLzezxDMaaNYlYMsEuz6MEG+CICz9PzEvY+szPch2KiGTOHcDXgQYAd18CXJbTiEREuqFMJtjv+ebOzKLALcAMYBIwy8wmNSs2A5gQvGaTXK0k1Rcp5Ef9Bj3Y5ZX5sYpIkwGDR7Co33kcveMJqnfvyHU4IpIZVe7+arN9xTUZXWNERKQAhN2DPR1Y7e5r3T0G3A/MbFZmJnCPJ80D+prZUAAzGwF8EPhlBuPMKm+sA6CiMvdPcmxuwDlfpMrqWfHEzbkORUQyY6eZjSdIQ83sEmBLbkMKh+Y4ikg+S3uSYyvLQFUDr7n7IuDFFo4PBzakbG8ETkyjzHCSvxR+CvwbbcyCN7PZJHu+GTVqVFu3kBsNdcS8hLI8XA5v/NEnseyxYxiz5rfE6r9BWXlFrkMSka65DrgdONLMNgFvA/+S25Ayy9WFLSIFoCM92NOAa0kmv8NJJrVnAneY2b+5+/UtnNNSH0Pz1rHFMmb2IWC7u7/WVlDufru7T3P3aYMGDWrvHrLOGmups7Jch9GqxpM+zxB2smjuHbkORUS6KPi28APAIOBIkm30aTkNKiTqwBaRfNaRBHsAcLy7/6u7/yvJhHsQcAZwdSvnbARGpmyPADanWeZU4MNmto7k0JKzzezeDsSbFyLxOmLkb4J9zJmXsDo6nqFLfkG8sbiGaop0F2bW28y+bmY/N7NzgRrgKmA18LHcRpdZes6MiBSCjiTYo4BYynYDMNrda4H6Vs6ZD0wws7FmVkZyNvucZmXmAFcGq4mcBFS7+xZ3/7q7j3D3McF5z7r7FR2INy9EGuuI5XEPtkUi7Jv2eUb6ZhY9rWXMRQrUb4CJwBvAp4GngUuBi929+byXoqAHzYhIPuvIg2Z+B8wzsz8G2xcB95lZD2B5Sye4e6OZXQ88BUSBO919mZldGxy/DZgLXEiyp6UGuKZTd5KnIvF6Gqw812G0aeq5H+ed+TfS97Wb8QuuwSKZnPsqIlkwzt2PBjCzXwI7gVHuvj+3YYmIdE9pJ9ju/l0zm0tyPJ8B17r7guBwq5No3H0uySQ6dd9tKe+d5MSctq79N+Bv6caaT6KJOhryuAcbIFpSwrZjPscJi/6DxX97kKlna9lckQLT0PTG3eNm9naxJtcaIiIihaCjj0p/DWhz0qEcqiReT0Mkv3uwAY698NNsXvwzql68ET/zY+rFFiksU81sX/DegMpg20j2Y/TOXWjh0AAREclnyqJCVpKop7EAEuzSsnI2Tf0SE+KrWfiUxmKLFBJ3j7p77+DVy91LUt4XVXKtDmwRKQRKsENW4vXEo4WxvvTxH/oM6yKjGDj/RhobYu2fICKSI5rjKCL5TAl2yMoS9cQLoAcbkmOx95z074xKbGLhY82fWC8ikj80FltE8pkS7JCVegOJAunBBjj2A5ezsmQiI5fcRF3twVyHIyI5Zmb9zewZM1sV/OzXSrl1ZvaGmS0yswUtlRER6S6UYIesjHoSJYWTYFskQsNZ/8kQdrLwwe/nOhwRyb0bgL+4+wTgL8F2a85y92PdfVrYQWmIiIjkMyXYISv3GF5ACTbAlFMvYmGP05i69pds3/R2rsMRkdyaCTTNfL4buDh3oYBrbIiIFAAl2CErp/ASbIDBl/wvURK888DXch2KiOTWYHffAhD8PKyVcg48bWavmdns8MNSF7aI5K8OrYMtHdMQq6fUElCACfawsUfy8ogrOHnTXbw5/88cecIHch2SiITEzP4MDGnh0H90oJpT3X2zmR0GPGNmb7r7861cbzYwG2DUqFEdjldEJN+pBztETZMErbQyx5F0ztRZ32E7/Sl58t+0bJ9IEXP3D7j7lBZefwS2mdlQgODn9lbq2Bz83A48Akxv43q3u/s0d582aNCgjsXaodIiIrmhBDtE9QWeYFf17MOG6f/F4fE1LHjgB7kOR0RyYw5wVfD+KuCPzQuYWQ8z69X0HjgPWBpmUJrkKCL5TAl2iGJ1NQBYWWEm2ADHX3AVC6tOYeqqW9i0dkWuwxGR7PshcK6ZrQLODbYxs2FmNjcoMxh4wcwWA68CT7j7k2EEozmOIlIIlGCHqKEu2YMdKeAE2yIRhl3+c+JE2f3AZ/FEItchiUgWufsudz/H3ScEP3cH+ze7+4XB+7XuPjV4TXb30Nf4VAe2iOQzJdghaqhP9mBHC3SISJPBI8azbPK/cnT9QhY8enOuwxERERHJa0qwQ9QYDBEpKa/KcSRdd8I/fYVlZccwafEP2LR2Wa7DEZFuS2NERCT/KcEOUUN9LQDRAh4i0iQSjTLgijuJW4QDv7uGhlh9rkMSkW5MkxxFJJ8pwQ5RojGZYJeUF36CDTBk1ARWnfBdJjauZMFvvpHrcESkG9IkRxEpBEqwQ5RoSPbyRksL70EzrXnfBz/F/D7nM/2dX7HspbntnyAiEgLTNEcRyWNKsEMUj9UBUFJWPAk2wJGfuI3NkaEMefpatm96O9fhiIiIiOQVJdghSjQme7BLiyzB7tWnP4mP3Uul17HnrsuoDyZzioiETSNERKQQhJ5gm9kFZrbSzFab2Q0tHDczuyk4vsTMjg/2jzSzv5rZCjNbZmZfDDvWTPNgiEhpeXEl2ACjj3ofb570f5nY+CaL7vhsrsMRkW5GkxxFJJ+FmmCbWRS4BZgBTAJmmdmkZsVmABOC12zg1mB/I/Cv7n4UcBJwXQvn5jUv0h7sJsfPuIaXh17BibseZd59oT9XQkREkxxFpCCE3YM9HVgdPOUrBtwPzGxWZiZwjyfNA/qa2VB33+LurwO4+35gBTA85HgzK0iwyyqKYxWRlkz/5M9YWHUq09+8kYVP35vrcESkm1APtojks7AT7OHAhpTtjbw3SW63jJmNAY4DXsl8iOHxeAyAsiJZpq8l0ZISjrzuAVaVHsFRL36JNxf8JdchiUgRc43CFpECEHaC3VIfQ/PWsc0yZtYT+APwJXff954LmM02swVmtmDHjh1dCjbjGuuJu1FSWpbrSEJV2aMXg2Y/ws7IAAY/fhVvLyuov4NEpABpmT4RyWdhJ9gbgZEp2yOAzemWMbNSksn1b9394ZYu4O63u/s0d582aNCgjAWeCRaPEaM012FkRf/DhmMff5gGSun90KWsX7ko1yGJiIiI5ETYCfZ8YIKZjTWzMuAyYE6zMnOAK4PVRE4Cqt19i5kZ8Ctghbv/OOQ4Q2HxehqseyTYAMPHTabu8kdwjKr7LmbD6jdyHZKIFBlNchSRQhBqgu3ujcD1wFMkJyk+6O7LzOxaM7s2KDYXWAusBu4APhfsPxX4OHC2mS0KXheGGW/GdaMe7CajjjiWg//8MFHiVNx7kYaLiEg4NEJERPJYSdgXcPe5JJPo1H23pbx34LoWznuBAm9CI/EYjd0swYbkGtnr//lRKh+4hAEPfYQ3D97NkdPPzXVYIlIE1IEtIoVAT3IMUSQR61ZDRFKNPup9xK95kmrrw+gnLmfxs/fnOiQRKSIF3fsiIkVPCXaIIokYjd00wQYYOnoiVdc+w6aSkUx57lrm3fttPJHIdVgiIiIioVKCHaLunmADDBg8gmFf/iuLe57OSat/woKfzaKu9mCuwxKRAuWa5SgiBUAJdoiiiRjxSHGvgZ2Oqp59OPYrj/LyqNmcUP0k7/zvmWxauyLXYYlIATM9ylFE8pgS7BBFEzHi3bwHu0kkGuXkT9zI6yf/nCGNm+h991kseOKOXIclIiIiknFKsENU4g3qwW7m+PM/zsFr/sqmsrFMm/9VXv3pLKr37Mx1WCJSYNR/LSL5TAl2iEoSDcQj6sFubujoiRz+b8/x8vBreN+eP1H/sxNY9Mzvch2WiIiISEYowQ5RicdIRMtzHUZeKikt4+RP/5S1F8/hQKQ3x774WV770Uy2b3o716GJSAozu9TMlplZwsymtVHuAjNbaWarzeyGsOLRHEcRKQRKsENU4g0kNESkTROOO4ORN7zKy2M+y9H7X6Dn7Sfy8l3/Tu3B/bkOTUSSlgIfBZ5vrYCZRYFbgBnAJGCWmU0KMyjNcRSRfKYEO0SlNOBRJdjtKS0r5+Srf8iOq17gzV4ncvL626i+8Vjm//EXNDbEch2eSLfm7ivcfWU7xaYDq919rbvHgPuBmaHEo2c5ikgBUIIdolIacPVgp234uKM4/quPsfz8+zkQ7cMJC7/Olh9MZcGcW5Voi+S34cCGlO2Nwb7QqANbRPKZEuwQlXqjerA7YdLJMxj3jfksPOUWYpFypr1+A1t+MJVXHryRmgPVuQ5PpOiY2Z/NbGkLr3R7oVvKd1vtajaz2Wa2wMwW7Nixo3NBi4jksZJcB1DMymjASzTJsTMi0SjHnXcFiXNmsfAv99HzlZ9w4vLvsW/5T5g35GJGz/giQ0dPzHWYIkXB3T/QxSo2AiNTtkcAm9u43u3A7QDTpk3r0JgPTXIUkUKgHuyQxBsbKbEEaBWRLmlKtA//j/m8OeMhVvecxrQt93HYnSey5IfnsODx26mrOZDrMEW6u/nABDMba2ZlwGXAnDAvqCc5ikg+U4Idklh9bfKNerAzwiIRjjzxPI7/6hx2fvJV5o/8BIfVrWfagq8R+58JvHrTFbzx/B9piNXnOlSRomJmHzGzjcDJwBNm9lSwf5iZzQVw90bgeuApYAXwoLsvCyMe9WCLSCHQEJGQxOrrqASsRGOwM23IqAkM+dSPScRvZOm8udS++hum7Hqaqmcfo/rZHrzV5zRKJn+YiadcRFXPPrkOV6SgufsjwCMt7N8MXJiyPReYm6241H8tIvlMCXZIGmLJHmxTD3ZoItEoU069CE69iLqaAyx84VEal81hYvUL9H7pKWIvfpFl5ZPYP+x0+h9zPuOPOZVoif6XFxERkXAp2whJrC6ZYEeUYGdFRVVPjjvvCjjvChpi9bwx70kOLn+KQTteYvK6W2DdLVTP6cG6yinUDD6eXoefwtipZ9CjV99chy4iHaARIiJSCJRgh6RRPdg5U1pWztFnzIQzkiuM7dy6gXUL/kRizd8YXL2EqetegXW3En/GWFMyhl29jiJx2GR6jT6OEUdNp0//QTm+AxFpl8aIiEgeU4IdksZgsl2kVAl2rg0cMpKBH5oNzAagetc21i15nto1L9Njx+tM2PsC/fbOhbeAZ2ArA9lWOY7a3uOwAePpMXQiA0dP4rDh44hEozm9F5HuzjXLUUQKgBLskDTG6gCIlFbkOBJprs+AwUw961I461IAPJFg59YNbF45n5oNiynZuZz+B1ZzxNZFVG6LwfLkeXVeypboMPaVD6auaiiJXsMo6TuSqkFj6Dt0DAOHjaW8oiqHdybSfZi6sEUkj4WeYJvZBcDPgCjwS3f/YbPjFhy/EKgBrnb319M5N5/FG5IJdlQ92HnPIhEGDhvNwGGjgUve3Z+Ix9m2ZR071i/n4Oa38J2rKN+/nl712xhV+yb9du17T13V9GCf9eFAST9qy/rTUDGARNVAIj0GUtL7MMp6DqSiVz8qe/WnR58B9OzTn9Iy/T8iki71X4tIIQg1wTazKHALcC7JJ33NN7M57r48pdgMYELwOhG4FTgxzXPzVjzowY6WVeY4EumsSDTK4BHjGTxifIvH62oOsGPz21RvWUvNzneI791I5OAOSup2URHbTf/adfQ+uJi+O/cTsdbTghov54D1oCbSg7pIT2IlPWmMVhIvqSBRUoWXVEJpFV7WAyutJFreAyuvoqS8JyUVPSgpqyRaWk5JWXnws5LSsnJKyyooKSunrLyS0tIyLKJl76V46DkzIpLPwu7Bng6sdve1AGZ2PzCTd790h2D7Hk8OrJtnZn3NbCgwJo1zu2z9m6+zfeW8TFYJQHzLUkA92MWsoqonIw8/mpGHH91muXhjI7t2bWXfri3U7ttF7MAeGg7uIVFbTaJ2L1ZXTSS2n5KGfZQ27KeisZqy2DbKvI4Kr6PC66kg1maSno6Yl9BACTErpZESGqyUBFESFiFBJHiffDmR4H0ET9nnkZLgZxS3Q19YBLdIkPkYWJDQWwTHkvstcsgxb77PDMOa1fPec61pX8p5/5Dy/t39lrLLmh1r/5yWyh7yJMFmx8v7DWHq2Ze1+lmIiEhxCzvBHg5sSNneSLKXur0yw9M8FzN7d/baqFGjOhzgltfnctJbN3b4vHQk3Og1cHgodUvhiJaUMGDwCAYMHtHpOjyRoLb2IHU1B6ir2Ues9iCx2gPEag8Qj9WSaIyRaKhL/mysxxtjeGM9xJt+NkBjDEvEsHjwSsQwT2CJxuRPj7/7ingcI0HE40QSMUo9ToQE5gmiHseIBz+T200puuGtv9wxIEICgp/JFPkfZSJ4l/+QyAfLS6eAEuxQHD08+fCos488LMeRiIi0LuwEu6Uv8Zr/9mytTDrn4u63A7cDTJs2rcO/mY+acS2bTvpIR09LS0WPPgzrQlIl0sQiESp79KKyRy9gaK7DCZ0nErh78EqQSCRwT6Tsb9rnJBKJlBM9tZJgl6cc9uBn4j37Dn2fUk8i5TjewjnvreswfXMVmqOG9ubN715ARalW9BGR/BV2gr0RGJmyPQLYnGaZsjTO7bI+/QbSp9/ATFcrIl1gkYjWiJBWKbkWkXwX9qyn+cAEMxtrZmXAZcCcZmXmAFda0klAtbtvSfNcEREREZG8EmoPtrs3mtn1wFMkl9q7092Xmdm1wfHbgLkkl+hbTXKZvmvaOjfMeEVEREREuir0dbDdfS7JJDp1320p7x24Lt1zRURERETymRbGFRERERHJICXYIiIiIiIZZKnLTRU6M9sBrO/EqQOBnRkOJ58U8/3p3gpXMd9fZ+9ttLsPynQw+awL7Xa+Keb/n1ui+y1uut/0tNpmF1WC3VlmtsDdp+U6jrAU8/3p3gpXMd9fMd+btKy7fea63+Km++06DREREREREckgJdgiIiIiIhmkBDvp9lwHELJivj/dW+Eq5vsr5nuTlnW3z1z3W9x0v12kMdgiIiIiIhmkHmwRERERkQzq9gm2mV1gZivNbLWZ3ZDreDLJzNaZ2RtmtsjMFuQ6nq4yszvNbLuZLU3Z19/MnjGzVcHPfrmMsbNaubdvm9mm4PNbZGYX5jLGzjKzkWb2VzNbYWbLzOyLwf6C/+zauLei+OykfcXWzrakmNvelhRze9xcMbfPrclWu92th4iYWRR4CzgX2AjMB2a5+/KcBpYhZrYOmObuRbGWpZmdARwA7nH3KcG+/wF2u/sPgz+Q+rn7v+cyzs5o5d6+DRxw9x/lMrauMrOhwFB3f93MegGvARcDV1Pgn10b9/YxiuCzk/YVWzvbkmJue1tSzO1xc8XcPrcmW+12d+/Bng6sdve17h4D7gdm5jgmaYW7Pw/sbrZ7JnB38P5ukv9ICk4r91YU3H2Lu78evN8PrACGUwSfXRv3JlI0irntbUkxt8fNFXP73JpstdvdPcEeDmxI2d5Icf1ydOBpM3vNzGbnOpiQDHb3LZD8RwMcluN4Mu16M1sSfGVZ8F/RmdkY4DjgFYrss2t2b1Bkn520qju0sy0pqn+/aSrqf9PF3D63Jsx2u7sn2NbCvmIaM3Oqux8PzACuC772ksJxKzAeOBbYAvxvTqPpIjPrCfwB+JK778t1PJnUwr0V1WcnbVI72z0U9b/pYm6fWxN2u93dE+yNwMiU7RHA5hzFknHuvjn4uR14hOSQmGKzLRhP1TSuanuO48kYd9/m7nF3TwB3UMCfn5mVkmzIfuvuDwe7i+Kza+neiumzk7Z1k3a2JUXx7zddxfxvupjb59Zko93u7gn2fGCCmY01szLgMmBOjmPKCDPrEQzex8x6AOcBS9s+qyDNAa4K3l8F/DGHsWRUU+MW+AgF+vmZmQG/Ala4+49TDhX8Z9favRXLZydt60btbEsK/t9vRxTrv+libp9bk612u1uvIgIQLMPyUyAK3Onu389tRJlhZuNI9qYAlAC/K/R7M7P7gDOBgcA24FvAo8CDwCjgHeBSdy+4ySmt3NuZJL+qcmAd8JmmMXGFxMxOA/4OvAEkgt3fIDnmraA/uzbubRZF8NlJ24qxnW1JMbe9LSnm9ri5Ym6fW5OtdrvbJ9giIiIiIpnU3YeIiIiIiIhklBJsEREREZEMUoItIiIiIpJBSrBFRERERDJICbaIiIiISAYpwRYRERERySAl2FIUzGyAmS0KXlvNbFPw/oCZ/SKE6/3azN42s2szUNeNQcxfzURsIiKFQO22FLOSXAcgkgnuvovkAvGY2beBA+7+o5Av+zV3/31XK3H3r5nZwUwEJCJSKNRuSzFTD7YUNTM708weD95/28zuNrOnzWydmX3UzP7HzN4wsyfNrDQo9z4ze87MXjOzp5o9PrW16/zazG41s7+a2Voze7+Z3WlmK8zs10GZaFBuaXDNL4d68yIiBUjtthQDJdjS3YwHPgjMBO4F/uruRwO1wAeDxvpm4BJ3fx9wJ5Duo4/7AWcDXwYeA34CTAaONrNjSfbUDHf3KcE178rUTYmIFDG121JwNEREups/uXuDmb0BRIEng/1vAGOAicAU4BkzIyizJc26H3N3D+re5u5vAJjZsqDu54BxZnYz8ATwdEbuSESkuKndloKjBFu6m3oAd0+YWYO7e7A/QfLfgwHL3P3kztYd1FWfsj8BlLj7HjObCpwPXAd8DPhEJ64jItKdqN2WgqMhIiKHWgkMMrOTAcys1MwmZ6JiMxsIRNz9D8B/Asdnol4RkW5O7bbkHfVgi6Rw95iZXQLcZGZ9SP4b+SmwLAPVDwfuMrOmP2y/noE6RUS6NbXbko/sH9+0iEi6ghnmj2diuaegvm+TnSWqRES6JbXbkk0aIiLSOdXAdzP1wALgCkBrqoqIhEfttmSNerBFRERERDJIPdgiIiIiIhmkBFtEREREJIOUYIuIiIiIZJASbBERERGRDFKCLSIiIiKSQf8fei2Xj5ce4ucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ampa = PlainChannel(nest.GetDefaults(\"ht_neuron\"), \"AMPA\")\n",
    "am_n, am_c = syn_voltage_clamp(ampa, [(25, -70.0)], nest_dt=0.001)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(am_n.times, am_n.g_AMPA, label=\"NEST\")\n",
    "plt.plot(am_c.times, am_c.g_AMPA, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_AMPA\")\n",
    "plt.title(\"AMPA Channel\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(am_n.times, (am_n.g_AMPA - am_c.g_AMPA) / am_c.g_AMPA)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"AMPA rel error\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaba_a = PlainChannel(nest.GetDefaults(\"ht_neuron\"), \"GABA_A\")\n",
    "ga_n, ga_c = syn_voltage_clamp(gaba_a, [(50, -70.0)])\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(ga_n.times, ga_n.g_GABA_A, label=\"NEST\")\n",
    "plt.plot(ga_c.times, ga_c.g_GABA_A, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_GABA_A\")\n",
    "plt.title(\"GABA_A Channel\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(ga_n.times, (ga_n.g_GABA_A - ga_c.g_GABA_A) / ga_c.g_GABA_A)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"GABA_A rel error\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaba_b = PlainChannel(nest.GetDefaults(\"ht_neuron\"), \"GABA_B\")\n",
    "gb_n, gb_c = syn_voltage_clamp(gaba_b, [(750, -70.0)])\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(gb_n.times, gb_n.g_GABA_B, label=\"NEST\")\n",
    "plt.plot(gb_c.times, gb_c.g_GABA_B, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_GABA_B\")\n",
    "plt.title(\"GABA_B Channel\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(gb_n.times, (gb_n.g_GABA_B - gb_c.g_GABA_B) / gb_c.g_GABA_B)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"GABA_B rel error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks good for all\n",
    "- For GABA_B the error is negligible even for dt = 0.1, since the time constants are large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NMDA Channel\n",
    "\n",
    "The equations for this channel are\n",
    "\n",
    "\\begin{align}\n",
    "\\bar{g}_{\\text{NMDA}}(t) &= m(V, t) g_{\\text{NMDA}}(t)     m(V, t)\\\\ &= a(V) m_{\\text{fast}}^*(V, t) + ( 1 - a(V) ) m_{\\text{slow}}^*(V, t)\\\\\n",
    "a(V)    &= 0.51 - 0.0028 V \\\\\n",
    "m^{\\infty}(V) &= \\frac{1}{ 1 + \\exp\\left( -S_{\\text{act}} ( V - V_{\\text{act}} ) \\right) } \\\\\n",
    "m_X^*(V, t) &= \\min(m^{\\infty}(V), m_X(V, t))\\\\\n",
    "\\frac{\\text{d}m_X}{\\text{d}t} &= \\frac{m^{\\infty}(V) - m_X }{ \\tau_{\\text{Mg}, X}}\n",
    "\\end{align} \n",
    "\n",
    "where $g_{\\text{NMDA}}(t)$ is the beta functions as for the other channels. In case of instantaneous unblocking, $m=m^{\\infty}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### NMDA with instantaneous unblocking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NMDAInstantChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp[\"receptor_types\"][receptor]\n",
    "        self.tau_1 = hp[\"tau_rise_\" + receptor]\n",
    "        self.tau_2 = hp[\"tau_decay_\" + receptor]\n",
    "        self.g_peak = hp[\"g_peak_\" + receptor]\n",
    "        self.E_rev = hp[\"E_rev_\" + receptor]\n",
    "        self.S_act = hp[\"S_act_NMDA\"]\n",
    "        self.V_act = hp[\"V_act_NMDA\"]\n",
    "        self.instantaneous = True\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1.0 / (1.0 + np.exp(-self.S_act * (V - self.V_act)))\n",
    "\n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        return self.g_peak * self.m_inf(V) * self.beta(t)\n",
    "\n",
    "    def I(self, t, V):\n",
    "        return -self.g(t) * (V - self.E_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmdai = NMDAInstantChannel(nest.GetDefaults(\"ht_neuron\"), \"NMDA\")\n",
    "ni_n, ni_c = syn_voltage_clamp(nmdai, [(50, -60.0), (50, -50.0), (50, -20.0), (50, 0.0), (50, -60.0)])\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(ni_n.times, ni_n.g_NMDA, label=\"NEST\")\n",
    "plt.plot(ni_c.times, ni_c.g_NMDA, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_NMDA\")\n",
    "plt.title(\"NMDA Channel (instant unblock)\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(ni_n.times, (ni_n.g_NMDA - ni_c.g_NMDA) / ni_c.g_NMDA)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"NMDA (inst) rel error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks good\n",
    "- Jumps are due to blocking/unblocking of Mg channels with changes in $V$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### NMDA with unblocking over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NMDAChannel(SynChannel):\n",
    "    def __init__(self, hp, receptor):\n",
    "        self.hp = hp\n",
    "        self.receptor = receptor\n",
    "        self.rec_code = hp[\"receptor_types\"][receptor]\n",
    "        self.tau_1 = hp[\"tau_rise_\" + receptor]\n",
    "        self.tau_2 = hp[\"tau_decay_\" + receptor]\n",
    "        self.g_peak = hp[\"g_peak_\" + receptor]\n",
    "        self.E_rev = hp[\"E_rev_\" + receptor]\n",
    "        self.S_act = hp[\"S_act_NMDA\"]\n",
    "        self.V_act = hp[\"V_act_NMDA\"]\n",
    "        self.tau_fast = hp[\"tau_Mg_fast_NMDA\"]\n",
    "        self.tau_slow = hp[\"tau_Mg_slow_NMDA\"]\n",
    "        self.instantaneous = False\n",
    "\n",
    "    def m_inf(self, V):\n",
    "        return 1.0 / (1.0 + np.exp(-self.S_act * (V - self.V_act)))\n",
    "\n",
    "    def dm(self, m, t, V, tau):\n",
    "        return (self.m_inf(V) - m) / tau\n",
    "\n",
    "    def g(self, t, V, mf0, ms0):\n",
    "        self.m_fast = si.odeint(self.dm, mf0, t, args=(V, self.tau_fast))\n",
    "        self.m_slow = si.odeint(self.dm, ms0, t, args=(V, self.tau_slow))\n",
    "        a = 0.51 - 0.0028 * V\n",
    "        m_inf = self.m_inf(V)\n",
    "        mfs = self.m_fast[:]\n",
    "        mfs[mfs > m_inf] = m_inf\n",
    "        mss = self.m_slow[:]\n",
    "        mss[mss > m_inf] = m_inf\n",
    "        m = np.squeeze(a * mfs + (1 - a) * mss)\n",
    "        return self.g_peak * m * self.beta(t)\n",
    "\n",
    "    def I(self, t, V):\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmda = NMDAChannel(nest.GetDefaults(\"ht_neuron\"), \"NMDA\")\n",
    "nm_n, nm_c = syn_voltage_clamp(nmda, [(50, -70.0), (50, -50.0), (50, -20.0), (50, 0.0), (50, -60.0)])\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(nm_n.times, nm_n.g_NMDA, label=\"NEST\")\n",
    "plt.plot(nm_c.times, nm_c.g_NMDA, label=\"Control\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"g_NMDA\")\n",
    "plt.title(\"NMDA Channel\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(nm_n.times, (nm_n.g_NMDA - nm_c.g_NMDA) / nm_c.g_NMDA)\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Rel error\")\n",
    "plt.title(\"NMDA rel error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks fine, too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synapse Model\n",
    "\n",
    "We test the synapse model by placing it between two parrot neurons, sending spikes with differing intervals and compare to expected weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recorded weights: [1.         0.875499   0.76977486 0.67387928 0.64996814 0.64689954\n",
      " 0.9123844 ]\n",
      "Expected weights: (1.0, 0.8754990013320011, 0.7697748551001631, 0.6738792820453234, 0.6499681432540876, 0.6468995408997453, 0.9123844012053444)\n",
      "Difference      : [0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "nest.ResetKernel()\n",
    "sp = nest.GetDefaults(\"ht_synapse\")\n",
    "P0 = sp[\"P\"]\n",
    "dP = sp[\"delta_P\"]\n",
    "tP = sp[\"tau_P\"]\n",
    "spike_times = [10.0, 12.0, 20.0, 20.5, 100.0, 200.0, 1000.0]\n",
    "expected = [(0.0, P0, P0)]\n",
    "for idx, t in enumerate(spike_times):\n",
    "    tlast, Psend, Ppost = expected[idx]\n",
    "    Psend = 1 - (1 - Ppost) * math.exp(-(t - tlast) / tP)\n",
    "    expected.append((t, Psend, (1 - dP) * Psend))\n",
    "expected_weights = list(zip(*expected[1:]))[1]\n",
    "\n",
    "sg = nest.Create(\"spike_generator\", params={\"spike_times\": spike_times})\n",
    "n = nest.Create(\"parrot_neuron\", 2)\n",
    "wr = nest.Create(\"weight_recorder\")\n",
    "\n",
    "nest.SetDefaults(\"ht_synapse\", {\"weight_recorder\": wr, \"weight\": 1.0})\n",
    "nest.Connect(sg, n[:1])\n",
    "nest.Connect(n[:1], n[1:], syn_spec=\"ht_synapse\")\n",
    "nest.Simulate(1200)\n",
    "\n",
    "rec_weights = wr.get(\"events\", \"weights\")\n",
    "\n",
    "print(\"Recorded weights:\", rec_weights)\n",
    "print(\"Expected weights:\", expected_weights)\n",
    "print(\"Difference      :\", np.array(rec_weights) - np.array(expected_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect agreement, synapse model looks fine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integration test: Neuron driven through all synapses\n",
    "\n",
    "We drive a Hill-Tononi neuron through pulse packets arriving at 1 second intervals, impinging through all synapse types. Compare this to Fig 5 of [HT05]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "nest.ResetKernel()\n",
    "nrn = nest.Create(\"ht_neuron\")\n",
    "ppg = nest.Create(\n",
    "    \"pulsepacket_generator\", n=4, params={\"pulse_times\": [700.0, 1700.0, 2700.0, 3700.0], \"activity\": 700, \"sdev\": 50.0}\n",
    ")\n",
    "pr = nest.Create(\"parrot_neuron\", n=4)\n",
    "mm = nest.Create(\n",
    "    \"multimeter\",\n",
    "    params={\n",
    "        \"interval\": 0.1,\n",
    "        \"record_from\": [\"V_m\", \"theta\", \"g_AMPA\", \"g_NMDA\", \"g_GABA_A\", \"g_GABA_B\", \"I_NaP\", \"I_KNa\", \"I_T\", \"I_h\"],\n",
    "    },\n",
    ")\n",
    "\n",
    "weights = {\"AMPA\": 25.0, \"NMDA\": 20.0, \"GABA_A\": 10.0, \"GABA_B\": 1.0}\n",
    "receptors = nest.GetDefaults(\"ht_neuron\")[\"receptor_types\"]\n",
    "\n",
    "nest.Connect(ppg, pr, \"one_to_one\")\n",
    "for p, (rec_name, rec_wgt) in zip(pr, weights.items()):\n",
    "    nest.Connect(\n",
    "        p, nrn, syn_spec={\"synapse_model\": \"ht_synapse\", \"receptor_type\": receptors[rec_name], \"weight\": rec_wgt}\n",
    "    )\n",
    "nest.Connect(mm, nrn)\n",
    "\n",
    "nest.Simulate(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = nest.GetStatus(mm)[0][\"events\"]\n",
    "t = data[\"times\"]\n",
    "\n",
    "\n",
    "def texify_name(name):\n",
    "    return r\"${}_{{\\mathrm{{{}}}}}$\".format(*name.split(\"_\"))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "\n",
    "Vax = fig.add_subplot(311)\n",
    "Vax.plot(t, data[\"V_m\"], \"k\", lw=1, label=r\"$V_m$\")\n",
    "Vax.plot(t, data[\"theta\"], \"r\", alpha=0.5, lw=1, label=r\"$\\Theta$\")\n",
    "Vax.set_ylabel(\"Potential [mV]\")\n",
    "Vax.legend(fontsize=\"small\")\n",
    "Vax.set_title(\"ht_neuron driven by sinousiodal Poisson processes\")\n",
    "\n",
    "Iax = fig.add_subplot(312)\n",
    "for iname, color in ((\"I_h\", \"blue\"), (\"I_KNa\", \"green\"), (\"I_NaP\", \"red\"), (\"I_T\", \"cyan\")):\n",
    "    Iax.plot(t, data[iname], color=color, lw=1, label=texify_name(iname))\n",
    "# Iax.set_ylim(-60, 60)\n",
    "Iax.legend(fontsize=\"small\")\n",
    "Iax.set_ylabel(\"Current [mV]\")\n",
    "\n",
    "Gax = fig.add_subplot(313)\n",
    "for gname, sgn, color in (\n",
    "    (\"g_AMPA\", 1, \"green\"),\n",
    "    (\"g_GABA_A\", -1, \"red\"),\n",
    "    (\"g_GABA_B\", -1, \"cyan\"),\n",
    "    (\"g_NMDA\", 1, \"magenta\"),\n",
    "):\n",
    "    Gax.plot(t, sgn * data[gname], lw=1, label=texify_name(gname), color=color)\n",
    "# Gax.set_ylim(-150, 150)\n",
    "Gax.legend(fontsize=\"small\")\n",
    "Gax.set_ylabel(\"Conductance\")\n",
    "Gax.set_xlabel(\"Time [ms]\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------\n",
    "### License\n",
    "\n",
    "This file is part of NEST. Copyright (C) 2004 The NEST Initiative\n",
    "\n",
    "NEST is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version.\n",
    "\n",
    "NEST is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
